Childhood Obesity epidemic analysis using classification algorithms

Size: px
Start display at page:

Download "Childhood Obesity epidemic analysis using classification algorithms"

Transcription

1 Childhood Obesity epidemic analysis using classification algorithms Suguna. M M.Phil. Scholar Trichy, Tamilnadu, India suguna15.9@gmail.com Abstract Obesity is the one of the most serious public health challenges of 21 st century globally there are more than one billion overweight adults and it is increasing three fold. In Today s world people get affected by many diseases where such disease leads to many other diseases. Obesity is such a condition that leads to many diseases. In this paper several data mining classification algorithms are used to detect the obesity and overweight conditions in children and detect the cause further. Keywords: Data mining, classification, childhood obesity, J48, CART, Naive Bayes I. INTRODUCTION Data mining is the process of extracting knowledge from large data. It is also called as knowledge in database. It is performed in iterative sequence. Classification maps data into predefined groups or classes often referred as supervised learning as classes are determined prior to examine. A classification task begins with a data set in which the class assignments are known. Medical diagnosis is a classification process. Fat is one of the main macronutrients. Fat plays an important role in preserving vital organs and it also maintains body temperature and it also store calories for future use. But however when it is stored in excess lead to obesity. Obesity is a serious worldwide health epidemic that affects one in four Americans. This phenomenon is global and about 30 million Indian are found to be obese and found to be double in next five years. Obesity is the condition that arise when body fat has accumulated to the extent that may have a negative effect on health, and it also leads to many other disease such as diabetes, sleep apnea, heart disease, stroke, gout, gallbladder disease, high blood pressure,osteoarthritis and even certain type of cancer. Childhood obesity is one of the most serious public health challenges of the 21st century. Children and adolescents who are obese are likely to be obese as adults and they are likely to have many health related and psychological problems. Childhood obesity is doubled in children and quadrupled in adults in the past years. The paper is organized as follows: section II as Related works, section III as Data set description, section IV as Proposed model, section V as Experimental results and finally section VI as Conclusion II. RELATED WORKS Adnan M.H.M [4] et al in their work provides the review of various data mining methods and its utilization in predictions of obesity and points outs the merits and demerits of particular data mining techniques. Adnan M.H.M [5] et al in their paper provides a novel framework for predictions of childhood obesity predictions using NBtree. Abduh Elbanna[6] et al in gastroenterology and surgery using data mining in obesity related gastrointestinal motility disorder; (IBS) and surgical bariatric approaches. Naive Bayes algorithm is used to obtain result and the rapid miner tool was employed. Alex Aussem [7] et al in their paper provided the framework for the analysis of visceral obesity and its determinants in women using Bayesians Networks. Archana Bhattarai [8] et al in their research work use text classification to predict obesity and its co-morbidities. Various algorithms such as Naive Bayes, Support Vector Machine (SVM), J48 and J48 and extraction were used. Syed Taghi Heydari[10] et al in their work performed a comparative study on detection of obesity using Artificial Neural Network and Logistics Regression. They predicted that Artificial Neural Network provides better result than Logistics Regression however they cited the requirement of a customized classifier for the Neural Networks.Sivaranjani.T [11] provides a comparative study on the prediction of obesity by using KNN and ID3 algorithms.she predicted that the ID3 algorithm works efficiently and provide more accurate result than the KNN algorithm by using Rapid Miner tool.shaoyan Zhang et al[12]in their paper provided a comparison of logistic regression with six data mining techniques such as decision trees (C4.5), association rules, Artificial Neural Networks (ANN), Naive Bayes, Bayesian networks and Support Vector Machines (SVM).They concluded that Support Vector Machine (SVM) provides more accurate result than the rest five data mining techniques. Sunita Soni et al [13] in their work discussed about the Case Based Reasoning (CBR).Three Data Mining techniques such as Nearest Neighborhood, Decision Tree and Bayesian Classification, were applied on distributed case bases for Case retrieval and Case adaptation. their work talks about the use of data mining technology in RES Publication 2012 Page 22 III. DATA SET DESCRIPTION The data are collected from the Child and Adolescent Health Measurement Initiative (CAHMI). DRC Indicator Dataset: 2007 National Survey of Children s Health. Data Resource Center for Child and Adolescent Health, ww.childhealthdata.org.. The objective of the data set is to

2 analysis the obesity among children whose age is between 10 to 17. The data set consists of 12 attributes that are used to predict the obesity. The detailed description of the data set are given in the below table data set description No Name of the Description attribute 1 CID Children Id 2 Age Age of the children 3 Gender Gender of the children 4 Weight Weight (pounds) 5 Height Height (mts) 6 Sleep hour Sleep time 7 Exercise time Exercise time 8 Time on Time spend in Computer computer 9 Watching TV Time spend on hours watching TV 10 Depression Depression level 11 Health status Status of health 12 Body Mass Index Value 13 CLS class 14 Category Category of the The attributes given here are based on the data type. The data type used here is numerical and nominal, here category and gender takes nominal values and the rest of the data takes the numerical values. IV. PROPOSED MODEL In the proposed method mainly decision tree is used for predicting the obesity from the given data set instances. Here the framework can be given as below, In the proposed model three different types of decision tree algorithms such as Simple Cart, J48 and NB Tree are applied on type obesity dataset in the WEKA tool and the performance is calculated. 4.1 Simple Cart Table 4.1: Proposed Framework INPUT DATA PREPROCESSING COLLECT DB DATA CLEANING DECISION TREE ALGORITHM OUTPUT CART is the Classification And Regression Tree it is greedy algorithm, it is used to build binary decision tree in that it chooses the locally best discriminatory feature at each stage in the process. As with ID3 entropy is used as a measure to choose the best splitting attribute and criterion. Here child is created for each sub category, only two children are created. The splitting is performed around what is determined to be the best split point. At each step an exhaustive search is performed to determine the best split. CART handles the missing data by simply ignoring that record in calculating the goodness of a split on that attribute. The tree stops growing when no split will improve the performance, it also contains a pruning strategy. 4.2 J48 Algorithm J48 builds decision trees from a set of labeled training data using the concept of information entropy. It uses the fact that each attribute of the data can be used to make a decision by splitting the data into smaller subsets. J48 examines the normalized information gain (difference in entropy) that results from choosing an attribute for splitting the data. To make the decision, the attribute with the highest RES Publication 2012 Page 23

3 normalized information gain is used. Then the algorithm recurs on the smaller subsets. The splitting procedure stops if all instances in a subset belong to the same class. Then a leaf node is created in the decision tree telling to choose that class. But it can also happen that none of the features give any information gain. In this case J48 creates a decision node higher up in the tree using the expected value of the class.j48 can handle both continuous and discrete attributes, training data with missing attribute value sand attributes with differing costs. Further it provides an option for pruning trees after creation. Fig 5.1 Performance of the Algorithms based on the time taken 4.3 NB Tree Naive bayes classification is a type of Bayesian classification. It is a simple classification technique. It assumes that the effect of an attribute value on a given class is independent of the values of the other attributes. This assumption is called class conditional independence. It exhibits high accuracy and speed when applied to large database. It is used to compare it results with decision tree networks and neural networks. V. EXPERIMENTAL RESULTS The given three types of decision tree algorithms like Simple Cart, J48 and NB Tree are applied on the obesity data set in WEKA and the performance of the algorithm are given based various factors. The performance can be obtained based on the time taken to build the tree and correctly classified instances Fig 5.2 Performance of the Algorithms based on the accuracy Table 5.1 Time taken by the algorithms to build the decision tree Name of the Algorithm Simple Cart Time Taken to build the decision tree 0.03 seconds J seconds NB Tree 0.45seconds RES Publication 2012 Page 24

4 The decision tree model for the obesity is given by the tree structure. Fig 5.3 Decision tree for obesity dataset Body Mass Index Table 5.3 confusion matrix for Simple CART,NB Tree and J <= Under weight The above decision tree algorithm predicts the class label. The final output will be patterns which are used to find the children fall in which Class based on it they are classified as Underweight, Normal, Overweight and obese or not. A Confusion Matrix is a useful visualization tool for analyzing the classifier accuracy. Structure of the confusion matrix can be given as below Where, Normal CLASS Table 5.2: Structure of the Confusion Matrix TP FP >1 <= >3 Over weight TN TP is True Positive, obese children correctly diagnosed. FP is False Positive, Normal people incorrectly identified as obese. TN is True Negative, Normal people correctly identified as healthy. FN is False Negative, obesity people incorrectly identified as healthy. The confusion matrix based on the execution of decision tree classification algorithm such as Simple Cart, NB tree and J48 FN Obese From the various factors such as time taken to build the decision tree, accuracy and confusion matrix we arrive at a conclusion that J48 algorithm provide better result. using WEKA tool are given below, RES Publication 2012 Page 25 VI. CONCLUSION Data mining plays a vital role in mining large database and its usage in determining hidden information is very useful in medical mining. In medical field classification techniques have high utility. This experimental model is built based as a test case on the training dataset. This experiment is successfully performed in children health training dataset with several data mining classification algorithms and found that J48 algorithm provides better performance with 100% of accuracy and minimum time taken. It is believed that the data mining can help in the obesity research and improve the health care of people with overweight and obesity. It can also be implemented using several classification techniques. REFERENCE [1] Han J. Kamber. M, Data Mining; Concepts and Techniques, Morgan Kaufmann Publishers. [2] Margaret H. Dunham, Data Mining Techniques and Algorithms, Prentice Hall Publishers. [3] Arun.K.Pujari, Data mining Techniques, University Press( India) Private Limited,2001 [4] Adnan, M.H.M., Husain.W, Rashid, N.A.A.Rashid, A survey on utilization of data mining for childhood obesity prediction, Information and Telecommunication Technologies(APSITT), th Asia -pacific symposium on, vol., no., pp.1,6, July [5] Adnan, M.H.M,Husain. W., Rashid, N.A.A.Rashid, A framework for childhood obesity classifications and predictions using NBtree, Information Technology in Asia (CITA 11), th International Conference, vol., no., pp.1,6, July [6] AbduhElbanna, AbdElrazek M., AlyAbdElrazek Is Advanced Statistical Computing Technology a Clue in Applied Medicine? A Study using Data Mining as a Predictor Technology in Gastroenterology & Bariatric Surgery; Novel Elbanna Operations, Global Journal of Computer Science

5 and Technology Software & Data EngineeringVolume 13 Issue 12 Version 1.0 Year [7] Aussem et al., Analysis of lifestyle and metabolic predictors of visceral obesity with Bayesian Networks. BMC Bioinformatics :487. [8] ArchanaBhattarai, VasileRus, DipankarDasgupta., Classification of Clinical Conditions: A Case Study on Prediction of obesity and its Co-morbidities, Department of Computer Science,The university of Memphis,209 Dunn Hall. [9] LalithaSarojaThota et al., A Review on Information Technology in Obesity Epidemic: Prediction and Prevention, International Journal of Advanced Research in Computer Science and Software Engineering 3(9), September , pp [10] SeyedTaghiHeydari et al., Comparison of Artificial Neural Networks with Logistic Regression for Detection of Obesity Springer April [11] T.Sivaranjani, Comparative Study on Obesity Based on ID3 and KNN International Journal of Advanced Research in Computer Science And Management Studies pp 389,396vol.2, Issue 9, September [12] Shaoyan Zhang, C.T., XiaojunZeng, Hong Qiao, Iain Buchan, John Keane, Comparing data mining methods withlogistic regression in childhood obesity prediction. Information Systems Frontiers, (4): p. 51. [13] Soni, S.; Pillai, J., "Usage of Nearest Neighborhood, Decision Tree and Bayesian Classification Techniques in Development of Weight Management Counseling System," Emerging Trends in Engineering and Technology, ICETET '08. First International Conference, vol., no., pp.691, 694, July BIOGRAPHY M.Suguna, M.Sc, B.Ed, M.Phil, completed M.phil in Bishop Heber College Trichy ( ). Paper Published: A Technical review on Obesity Analysis using classification algorithms, International Journal of Applied Engineering Research, ISSN Vol. 10 No.55 (2015). RES Publication 2012 Page 26

Rule Learning With Negation: Issues Regarding Effectiveness

Rule Learning With Negation: Issues Regarding Effectiveness Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United

More information

Rule Learning with Negation: Issues Regarding Effectiveness

Rule Learning with Negation: Issues Regarding Effectiveness Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX

More information

Comparison of EM and Two-Step Cluster Method for Mixed Data: An Application

Comparison of EM and Two-Step Cluster Method for Mixed Data: An Application International Journal of Medical Science and Clinical Inventions 4(3): 2768-2773, 2017 DOI:10.18535/ijmsci/ v4i3.8 ICV 2015: 52.82 e-issn: 2348-991X, p-issn: 2454-9576 2017, IJMSCI Research Article Comparison

More information

Reducing Features to Improve Bug Prediction

Reducing Features to Improve Bug Prediction Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science

More information

The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X

The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, 2013 10.12753/2066-026X-13-154 DATA MINING SOLUTIONS FOR DETERMINING STUDENT'S PROFILE Adela BÂRA,

More information

Applications of data mining algorithms to analysis of medical data

Applications of data mining algorithms to analysis of medical data Master Thesis Software Engineering Thesis no: MSE-2007:20 August 2007 Applications of data mining algorithms to analysis of medical data Dariusz Matyja School of Engineering Blekinge Institute of Technology

More information

CS Machine Learning

CS Machine Learning CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing

More information

ScienceDirect. A Framework for Clustering Cardiac Patient s Records Using Unsupervised Learning Techniques

ScienceDirect. A Framework for Clustering Cardiac Patient s Records Using Unsupervised Learning Techniques Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 98 (2016 ) 368 373 The 6th International Conference on Current and Future Trends of Information and Communication Technologies

More information

A Case Study: News Classification Based on Term Frequency

A Case Study: News Classification Based on Term Frequency A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center

More information

Mining Association Rules in Student s Assessment Data

Mining Association Rules in Student s Assessment Data www.ijcsi.org 211 Mining Association Rules in Student s Assessment Data Dr. Varun Kumar 1, Anupama Chadha 2 1 Department of Computer Science and Engineering, MVN University Palwal, Haryana, India 2 Anupama

More information

Australian Journal of Basic and Applied Sciences

Australian Journal of Basic and Applied Sciences AENSI Journals Australian Journal of Basic and Applied Sciences ISSN:1991-8178 Journal home page: www.ajbasweb.com Feature Selection Technique Using Principal Component Analysis For Improving Fuzzy C-Mean

More information

Twitter Sentiment Classification on Sanders Data using Hybrid Approach

Twitter Sentiment Classification on Sanders Data using Hybrid Approach IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118-123 www.iosrjournals.org Twitter Sentiment Classification on Sanders

More information

Learning From the Past with Experiment Databases

Learning From the Past with Experiment Databases Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University

More information

EDEXCEL FUNCTIONAL SKILLS PILOT TEACHER S NOTES. Maths Level 2. Chapter 4. Working with measures

EDEXCEL FUNCTIONAL SKILLS PILOT TEACHER S NOTES. Maths Level 2. Chapter 4. Working with measures EDEXCEL FUNCTIONAL SKILLS PILOT TEACHER S NOTES Maths Level 2 Chapter 4 Working with measures SECTION G 1 Time 2 Temperature 3 Length 4 Weight 5 Capacity 6 Conversion between metric units 7 Conversion

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,

More information

OCR for Arabic using SIFT Descriptors With Online Failure Prediction

OCR for Arabic using SIFT Descriptors With Online Failure Prediction OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,

More information

Process Evaluations for a Multisite Nutrition Education Program

Process Evaluations for a Multisite Nutrition Education Program Process Evaluations for a Multisite Nutrition Education Program Paul Branscum 1 and Gail Kaye 2 1 The University of Oklahoma 2 The Ohio State University Abstract Process evaluations are an often-overlooked

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

Disambiguation of Thai Personal Name from Online News Articles

Disambiguation of Thai Personal Name from Online News Articles Disambiguation of Thai Personal Name from Online News Articles Phaisarn Sutheebanjard Graduate School of Information Technology Siam University Bangkok, Thailand mr.phaisarn@gmail.com Abstract Since online

More information

CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH

CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH ISSN: 0976-3104 Danti and Bhushan. ARTICLE OPEN ACCESS CLASSIFICATION OF TEXT DOCUMENTS USING INTEGER REPRESENTATION AND REGRESSION: AN INTEGRATED APPROACH Ajit Danti 1 and SN Bharath Bhushan 2* 1 Department

More information

Lecture 1: Basic Concepts of Machine Learning

Lecture 1: Basic Concepts of Machine Learning Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010

More information

Modeling function word errors in DNN-HMM based LVCSR systems

Modeling function word errors in DNN-HMM based LVCSR systems Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford

More information

Assignment 1: Predicting Amazon Review Ratings

Assignment 1: Predicting Amazon Review Ratings Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for

More information

Issues in the Mining of Heart Failure Datasets

Issues in the Mining of Heart Failure Datasets International Journal of Automation and Computing 11(2), April 2014, 162-179 DOI: 10.1007/s11633-014-0778-5 Issues in the Mining of Heart Failure Datasets Nongnuch Poolsawad 1 Lisa Moore 1 Chandrasekhar

More information

Speech Emotion Recognition Using Support Vector Machine

Speech Emotion Recognition Using Support Vector Machine Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents

More information

Switchboard Language Model Improvement with Conversational Data from Gigaword

Switchboard Language Model Improvement with Conversational Data from Gigaword Katholieke Universiteit Leuven Faculty of Engineering Master in Artificial Intelligence (MAI) Speech and Language Technology (SLT) Switchboard Language Model Improvement with Conversational Data from Gigaword

More information

Human Emotion Recognition From Speech

Human Emotion Recognition From Speech RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati

More information

Linking Task: Identifying authors and book titles in verbose queries

Linking Task: Identifying authors and book titles in verbose queries Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,

More information

Calibration of Confidence Measures in Speech Recognition

Calibration of Confidence Measures in Speech Recognition Submitted to IEEE Trans on Audio, Speech, and Language, July 2010 1 Calibration of Confidence Measures in Speech Recognition Dong Yu, Senior Member, IEEE, Jinyu Li, Member, IEEE, Li Deng, Fellow, IEEE

More information

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and

More information

Content-based Image Retrieval Using Image Regions as Query Examples

Content-based Image Retrieval Using Image Regions as Query Examples Content-based Image Retrieval Using Image Regions as Query Examples D. N. F. Awang Iskandar James A. Thom S. M. M. Tahaghoghi School of Computer Science and Information Technology, RMIT University Melbourne,

More information

Indian Institute of Technology, Kanpur

Indian Institute of Technology, Kanpur Indian Institute of Technology, Kanpur Course Project - CS671A POS Tagging of Code Mixed Text Ayushman Sisodiya (12188) {ayushmn@iitk.ac.in} Donthu Vamsi Krishna (15111016) {vamsi@iitk.ac.in} Sandeep Kumar

More information

Modeling function word errors in DNN-HMM based LVCSR systems

Modeling function word errors in DNN-HMM based LVCSR systems Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford

More information

A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and

A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and Planning Overview Motivation for Analyses Analyses and

More information

Activity Recognition from Accelerometer Data

Activity Recognition from Accelerometer Data Activity Recognition from Accelerometer Data Nishkam Ravi and Nikhil Dandekar and Preetham Mysore and Michael L. Littman Department of Computer Science Rutgers University Piscataway, NJ 08854 {nravi,nikhild,preetham,mlittman}@cs.rutgers.edu

More information

Wellness Committee Action Plan. Developed in compliance with the Child Nutrition and Women, Infant and Child (WIC) Reauthorization Act of 2004

Wellness Committee Action Plan. Developed in compliance with the Child Nutrition and Women, Infant and Child (WIC) Reauthorization Act of 2004 Wellness Committee Action Plan Developed in compliance with the Child Nutrition and Women, Infant and Child (WIC) Reauthorization Act of 2004 PREAMBLE Overweight children and adolescents have become a

More information

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics Machine Learning from Garden Path Sentences: The Application of Computational Linguistics http://dx.doi.org/10.3991/ijet.v9i6.4109 J.L. Du 1, P.F. Yu 1 and M.L. Li 2 1 Guangdong University of Foreign Studies,

More information

AUTOMATED TROUBLESHOOTING OF MOBILE NETWORKS USING BAYESIAN NETWORKS

AUTOMATED TROUBLESHOOTING OF MOBILE NETWORKS USING BAYESIAN NETWORKS AUTOMATED TROUBLESHOOTING OF MOBILE NETWORKS USING BAYESIAN NETWORKS R.Barco 1, R.Guerrero 2, G.Hylander 2, L.Nielsen 3, M.Partanen 2, S.Patel 4 1 Dpt. Ingeniería de Comunicaciones. Universidad de Málaga.

More information

Feature Selection based on Sampling and C4.5 Algorithm to Improve the Quality of Text Classification using Naïve Bayes

Feature Selection based on Sampling and C4.5 Algorithm to Improve the Quality of Text Classification using Naïve Bayes Feature Selection based on Sampling and C4.5 Algorithm to Improve the Quality of Text Classification using Naïve Bayes Viviana Molano 1, Carlos Cobos 1, Martha Mendoza 1, Enrique Herrera-Viedma 2, and

More information

Data Fusion Through Statistical Matching

Data Fusion Through Statistical Matching A research and education initiative at the MIT Sloan School of Management Data Fusion Through Statistical Matching Paper 185 Peter Van Der Puttan Joost N. Kok Amar Gupta January 2002 For more information,

More information

CSL465/603 - Machine Learning

CSL465/603 - Machine Learning CSL465/603 - Machine Learning Fall 2016 Narayanan C Krishnan ckn@iitrpr.ac.in Introduction CSL465/603 - Machine Learning 1 Administrative Trivia Course Structure 3-0-2 Lecture Timings Monday 9.55-10.45am

More information

A NEW ALGORITHM FOR GENERATION OF DECISION TREES

A NEW ALGORITHM FOR GENERATION OF DECISION TREES TASK QUARTERLY 8 No 2(2004), 1001 1005 A NEW ALGORITHM FOR GENERATION OF DECISION TREES JERZYW.GRZYMAŁA-BUSSE 1,2,ZDZISŁAWS.HIPPE 2, MAKSYMILIANKNAP 2 ANDTERESAMROCZEK 2 1 DepartmentofElectricalEngineeringandComputerScience,

More information

Netpix: A Method of Feature Selection Leading. to Accurate Sentiment-Based Classification Models

Netpix: A Method of Feature Selection Leading. to Accurate Sentiment-Based Classification Models Netpix: A Method of Feature Selection Leading to Accurate Sentiment-Based Classification Models 1 Netpix: A Method of Feature Selection Leading to Accurate Sentiment-Based Classification Models James B.

More information

Cross-lingual Short-Text Document Classification for Facebook Comments

Cross-lingual Short-Text Document Classification for Facebook Comments 2014 International Conference on Future Internet of Things and Cloud Cross-lingual Short-Text Document Classification for Facebook Comments Mosab Faqeeh, Nawaf Abdulla, Mahmoud Al-Ayyoub, Yaser Jararweh

More information

Fuzzy rule-based system applied to risk estimation of cardiovascular patients

Fuzzy rule-based system applied to risk estimation of cardiovascular patients Fuzzy rule-based system applied to risk estimation of cardiovascular patients Jan Bohacik, Department of Computer Science, University of Hull, Hull, HU6 7RX, United Kingdom and Department of Informatics,

More information

Universidade do Minho Escola de Engenharia

Universidade do Minho Escola de Engenharia Universidade do Minho Escola de Engenharia Universidade do Minho Escola de Engenharia Dissertação de Mestrado Knowledge Discovery is the nontrivial extraction of implicit, previously unknown, and potentially

More information

THE ROLE OF DECISION TREES IN NATURAL LANGUAGE PROCESSING

THE ROLE OF DECISION TREES IN NATURAL LANGUAGE PROCESSING SISOM & ACOUSTICS 2015, Bucharest 21-22 May THE ROLE OF DECISION TREES IN NATURAL LANGUAGE PROCESSING MarilenaăLAZ R 1, Diana MILITARU 2 1 Military Equipment and Technologies Research Agency, Bucharest,

More information

Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks

Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks Devendra Singh Chaplot, Eunhee Rhim, and Jihie Kim Samsung Electronics Co., Ltd. Seoul, South Korea {dev.chaplot,eunhee.rhim,jihie.kim}@samsung.com

More information

Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning

Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning Hendrik Blockeel and Joaquin Vanschoren Computer Science Dept., K.U.Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium

More information

Word Segmentation of Off-line Handwritten Documents

Word Segmentation of Off-line Handwritten Documents Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department

More information

Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees

Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees Mariusz Łapczy ski 1 and Bartłomiej Jefma ski 2 1 The Chair of Market Analysis and Marketing Research,

More information

Analysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier

Analysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 2, Ver.1 (Mar - Apr.2015), PP 55-61 www.iosrjournals.org Analysis of Emotion

More information

Semi-Supervised Face Detection

Semi-Supervised Face Detection Semi-Supervised Face Detection Nicu Sebe, Ira Cohen 2, Thomas S. Huang 3, Theo Gevers Faculty of Science, University of Amsterdam, The Netherlands 2 HP Research Labs, USA 3 Beckman Institute, University

More information

Evolutive Neural Net Fuzzy Filtering: Basic Description

Evolutive Neural Net Fuzzy Filtering: Basic Description Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:

More information

A Model to Predict 24-Hour Urinary Creatinine Level Using Repeated Measurements

A Model to Predict 24-Hour Urinary Creatinine Level Using Repeated Measurements Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2006 A Model to Predict 24-Hour Urinary Creatinine Level Using Repeated Measurements Donna S. Kroos Virginia

More information

Data Fusion Models in WSNs: Comparison and Analysis

Data Fusion Models in WSNs: Comparison and Analysis Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) Data Fusion s in WSNs: Comparison and Analysis Marwah M Almasri, and Khaled M Elleithy, Senior Member,

More information

Truth Inference in Crowdsourcing: Is the Problem Solved?

Truth Inference in Crowdsourcing: Is the Problem Solved? Truth Inference in Crowdsourcing: Is the Problem Solved? Yudian Zheng, Guoliang Li #, Yuanbing Li #, Caihua Shan, Reynold Cheng # Department of Computer Science, Tsinghua University Department of Computer

More information

On-Line Data Analytics

On-Line Data Analytics International Journal of Computer Applications in Engineering Sciences [VOL I, ISSUE III, SEPTEMBER 2011] [ISSN: 2231-4946] On-Line Data Analytics Yugandhar Vemulapalli #, Devarapalli Raghu *, Raja Jacob

More information

Improving Simple Bayes. Abstract. The simple Bayesian classier (SBC), sometimes called

Improving Simple Bayes. Abstract. The simple Bayesian classier (SBC), sometimes called Improving Simple Bayes Ron Kohavi Barry Becker Dan Sommereld Data Mining and Visualization Group Silicon Graphics, Inc. 2011 N. Shoreline Blvd. Mountain View, CA 94043 fbecker,ronnyk,sommdag@engr.sgi.com

More information

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina

More information

Learning Methods in Multilingual Speech Recognition

Learning Methods in Multilingual Speech Recognition Learning Methods in Multilingual Speech Recognition Hui Lin Department of Electrical Engineering University of Washington Seattle, WA 98125 linhui@u.washington.edu Li Deng, Jasha Droppo, Dong Yu, and Alex

More information

Multivariate k-nearest Neighbor Regression for Time Series data -

Multivariate k-nearest Neighbor Regression for Time Series data - Multivariate k-nearest Neighbor Regression for Time Series data - a novel Algorithm for Forecasting UK Electricity Demand ISF 2013, Seoul, Korea Fahad H. Al-Qahtani Dr. Sven F. Crone Management Science,

More information

Multi-Lingual Text Leveling

Multi-Lingual Text Leveling Multi-Lingual Text Leveling Salim Roukos, Jerome Quin, and Todd Ward IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 {roukos,jlquinn,tward}@us.ibm.com Abstract. Determining the language proficiency

More information

Computerized Adaptive Psychological Testing A Personalisation Perspective

Computerized Adaptive Psychological Testing A Personalisation Perspective Psychology and the internet: An European Perspective Computerized Adaptive Psychological Testing A Personalisation Perspective Mykola Pechenizkiy mpechen@cc.jyu.fi Introduction Mixed Model of IRT and ES

More information

Using Web Searches on Important Words to Create Background Sets for LSI Classification

Using Web Searches on Important Words to Create Background Sets for LSI Classification Using Web Searches on Important Words to Create Background Sets for LSI Classification Sarah Zelikovitz and Marina Kogan College of Staten Island of CUNY 2800 Victory Blvd Staten Island, NY 11314 Abstract

More information

Analyzing sentiments in tweets for Tesla Model 3 using SAS Enterprise Miner and SAS Sentiment Analysis Studio

Analyzing sentiments in tweets for Tesla Model 3 using SAS Enterprise Miner and SAS Sentiment Analysis Studio SCSUG Student Symposium 2016 Analyzing sentiments in tweets for Tesla Model 3 using SAS Enterprise Miner and SAS Sentiment Analysis Studio Praneth Guggilla, Tejaswi Jha, Goutam Chakraborty, Oklahoma State

More information

SAGES 2017 ANNUAL MEETING SESSION DESIGN FORM - SAMPLE

SAGES 2017 ANNUAL MEETING SESSION DESIGN FORM - SAMPLE Fictitious Examples are listed below in orange: SAGES 2017 ANNUAL MEETING SESSION DESIGN FORM - SAMPLE SESSION CHAIR: Raul Rosenthal SESSION CO-CHAIR: Daniel Jones SESSION TITLE: PG Bariatric: What Every

More information

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Ajith Abraham School of Business Systems, Monash University, Clayton, Victoria 3800, Australia. Email: ajith.abraham@ieee.org

More information

Mining Student Evolution Using Associative Classification and Clustering

Mining Student Evolution Using Associative Classification and Clustering Mining Student Evolution Using Associative Classification and Clustering 19 Mining Student Evolution Using Associative Classification and Clustering Kifaya S. Qaddoum, Faculty of Information, Technology

More information

Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages

Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages Iterative Cross-Training: An Algorithm for Learning from Unlabeled Web Pages Nuanwan Soonthornphisaj 1 and Boonserm Kijsirikul 2 Machine Intelligence and Knowledge Discovery Laboratory Department of Computer

More information

GRADUATE STUDENT HANDBOOK Master of Science Programs in Biostatistics

GRADUATE STUDENT HANDBOOK Master of Science Programs in Biostatistics 2017-2018 GRADUATE STUDENT HANDBOOK Master of Science Programs in Biostatistics Entrance requirements, program descriptions, degree requirements and other program policies for Biostatistics Master s Programs

More information

MYCIN. The MYCIN Task

MYCIN. The MYCIN Task MYCIN Developed at Stanford University in 1972 Regarded as the first true expert system Assists physicians in the treatment of blood infections Many revisions and extensions over the years The MYCIN Task

More information

Multi-label Classification via Multi-target Regression on Data Streams

Multi-label Classification via Multi-target Regression on Data Streams Multi-label Classification via Multi-target Regression on Data Streams Aljaž Osojnik 1,2, Panče Panov 1, and Sašo Džeroski 1,2,3 1 Jožef Stefan Institute, Jamova cesta 39, Ljubljana, Slovenia 2 Jožef Stefan

More information

INPE São José dos Campos

INPE São José dos Campos INPE-5479 PRE/1778 MONLINEAR ASPECTS OF DATA INTEGRATION FOR LAND COVER CLASSIFICATION IN A NEDRAL NETWORK ENVIRONNENT Maria Suelena S. Barros Valter Rodrigues INPE São José dos Campos 1993 SECRETARIA

More information

COMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS

COMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS COMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS L. Descalço 1, Paula Carvalho 1, J.P. Cruz 1, Paula Oliveira 1, Dina Seabra 2 1 Departamento de Matemática, Universidade de Aveiro (PORTUGAL)

More information

A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices

A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices Article A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices Yerim Choi 1, Yu-Mi Jeon 2, Lin Wang 3, * and Kwanho Kim 2, * 1 Department of Industrial and Management

More information

Softprop: Softmax Neural Network Backpropagation Learning

Softprop: Softmax Neural Network Backpropagation Learning Softprop: Softmax Neural Networ Bacpropagation Learning Michael Rimer Computer Science Department Brigham Young University Provo, UT 84602, USA E-mail: mrimer@axon.cs.byu.edu Tony Martinez Computer Science

More information

Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming

Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming Data Mining VI 205 Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming C. Romero, S. Ventura, C. Hervás & P. González Universidad de Córdoba, Campus Universitario de

More information

A Neural Network GUI Tested on Text-To-Phoneme Mapping

A Neural Network GUI Tested on Text-To-Phoneme Mapping A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis

More information

Fragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing

Fragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing Fragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing D. Indhumathi Research Scholar Department of Information Technology

More information

Large-Scale Web Page Classification. Sathi T Marath. Submitted in partial fulfilment of the requirements. for the degree of Doctor of Philosophy

Large-Scale Web Page Classification. Sathi T Marath. Submitted in partial fulfilment of the requirements. for the degree of Doctor of Philosophy Large-Scale Web Page Classification by Sathi T Marath Submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Dalhousie University Halifax, Nova Scotia November 2010

More information

Semi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.

Semi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17. Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link

More information

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering

More information

POLA: a student modeling framework for Probabilistic On-Line Assessment of problem solving performance

POLA: a student modeling framework for Probabilistic On-Line Assessment of problem solving performance POLA: a student modeling framework for Probabilistic On-Line Assessment of problem solving performance Cristina Conati, Kurt VanLehn Intelligent Systems Program University of Pittsburgh Pittsburgh, PA,

More information

Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling

Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Notebook for PAN at CLEF 2013 Andrés Alfonso Caurcel Díaz 1 and José María Gómez Hidalgo 2 1 Universidad

More information

Product Feature-based Ratings foropinionsummarization of E-Commerce Feedback Comments

Product Feature-based Ratings foropinionsummarization of E-Commerce Feedback Comments Product Feature-based Ratings foropinionsummarization of E-Commerce Feedback Comments Vijayshri Ramkrishna Ingale PG Student, Department of Computer Engineering JSPM s Imperial College of Engineering &

More information

CS 446: Machine Learning

CS 446: Machine Learning CS 446: Machine Learning Introduction to LBJava: a Learning Based Programming Language Writing classifiers Christos Christodoulopoulos Parisa Kordjamshidi Motivation 2 Motivation You still have not learnt

More information

CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE. Mingon Kang, PhD Computer Science, Kennesaw State University

CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE. Mingon Kang, PhD Computer Science, Kennesaw State University CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE Mingon Kang, PhD Computer Science, Kennesaw State University Self Introduction Mingon Kang, PhD Homepage: http://ksuweb.kennesaw.edu/~mkang9

More information

Using dialogue context to improve parsing performance in dialogue systems

Using dialogue context to improve parsing performance in dialogue systems Using dialogue context to improve parsing performance in dialogue systems Ivan Meza-Ruiz and Oliver Lemon School of Informatics, Edinburgh University 2 Buccleuch Place, Edinburgh I.V.Meza-Ruiz@sms.ed.ac.uk,

More information

Beyond the Pipeline: Discrete Optimization in NLP

Beyond the Pipeline: Discrete Optimization in NLP Beyond the Pipeline: Discrete Optimization in NLP Tomasz Marciniak and Michael Strube EML Research ggmbh Schloss-Wolfsbrunnenweg 33 69118 Heidelberg, Germany http://www.eml-research.de/nlp Abstract We

More information

Executive Guide to Simulation for Health

Executive Guide to Simulation for Health Executive Guide to Simulation for Health Simulation is used by Healthcare and Human Service organizations across the World to improve their systems of care and reduce costs. Simulation offers evidence

More information

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 11/2007, ISSN 1642-6037 Marek WIŚNIEWSKI *, Wiesława KUNISZYK-JÓŹKOWIAK *, Elżbieta SMOŁKA *, Waldemar SUSZYŃSKI * HMM, recognition, speech, disorders

More information

Linking the Ohio State Assessments to NWEA MAP Growth Tests *

Linking the Ohio State Assessments to NWEA MAP Growth Tests * Linking the Ohio State Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. August 2016 Introduction Northwest Evaluation Association (NWEA

More information

Genre classification on German novels

Genre classification on German novels Genre classification on German novels Lena Hettinger, Martin Becker, Isabella Reger, Fotis Jannidis and Andreas Hotho Data Mining and Information Retrieval Group, University of Würzburg Email: {hettinger,

More information

Historical maintenance relevant information roadmap for a self-learning maintenance prediction procedural approach

Historical maintenance relevant information roadmap for a self-learning maintenance prediction procedural approach IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Historical maintenance relevant information roadmap for a self-learning maintenance prediction procedural approach To cite this

More information

Ph.D in Advance Machine Learning (computer science) PhD submitted, degree to be awarded on convocation, sept B.Tech in Computer science and

Ph.D in Advance Machine Learning (computer science) PhD submitted, degree to be awarded on convocation, sept B.Tech in Computer science and Name Qualification Sonia Thomas Ph.D in Advance Machine Learning (computer science) PhD submitted, degree to be awarded on convocation, sept. 2016. M.Tech in Computer science and Engineering. B.Tech in

More information

GUIDELINES FOR COMBINED TRAINING IN PEDIATRICS AND MEDICAL GENETICS LEADING TO DUAL CERTIFICATION

GUIDELINES FOR COMBINED TRAINING IN PEDIATRICS AND MEDICAL GENETICS LEADING TO DUAL CERTIFICATION GUIDELINES FOR COMBINED TRAINING IN PEDIATRICS AND MEDICAL GENETICS LEADING TO DUAL CERTIFICATION PREAMBLE This document is intended to provide educational guidance to program directors in pediatrics and

More information