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Cluster Validation Analysis on Attribute Relative of Soft-Set Theory Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Data Mining Techniques for Classification of Childhood Obesity Among Year 6 School Children

Authors : Fadzli Syed Abdullah, Nor Saidah Abd Manan, Aryati Ahmad, Sharifah Wajihah Wafa, Mohd Razif Shahril, Nurzaime Zulaily, Rahmah Mohd Amin, Amran Ahmed

Published in: Recent Advances on Soft Computing and Data Mining

Publisher: Springer International Publishing

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Abstract

Today, data mining is broadly applied in many fields, including healthcare and medical fields. Obesity problem among children is one of the issues commonly explored using data mining techniques. In this paper, the classification of childhood obesity among year six school children from two districts in Terengganu, Malaysia is discussed. The data were collected from two main sources; a Standard Kecergasan Fizikal Kebangsaan untuk Murid Sekolah Malaysia/National Physical Fitness Standard for Malaysian School Children (SEGAK) Assessment Program and a set of distributed questionnaire. From the collected data, 4,245 complete data sets were promptly analyzed. The data preprocessing and feature selection were implemented to the data sets. The classification techniques, namely Bayesian Network, Decision Tree, Neural Networks and Support Vector Machine (SVM) were implemented and compared on the data sets. This paper presents the evaluation of several feature selection methods based on different classifiers.

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Metadata
Title
Data Mining Techniques for Classification of Childhood Obesity Among Year 6 School Children
Authors
Fadzli Syed Abdullah
Nor Saidah Abd Manan
Aryati Ahmad
Sharifah Wajihah Wafa
Mohd Razif Shahril
Nurzaime Zulaily
Rahmah Mohd Amin
Amran Ahmed
Copyright Year
2017
Book
Recent Advances on Soft Computing and Data Mining

Print ISBN: 978-3-319-51279-2

Electronic ISBN: 978-3-319-51281-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-51281-5_47

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