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

Syed Abdullah, Prof. Madya Dr. Engku Fadzli Hasan and Syed Saadun Tarek Wafa, Prof. Madya Dr. Sharifah Wajihah Wafa and Mohd Amin, Prof. Dr. Rahmah and Shahril, Dr. Mohd Razif and Ahmad, Prof. Madya Dr. Aryati (2016) Data Mining Techniques for Classification of Childhood Obesity Among Year 6 School Children. In: Recent Advances on Soft Computing and Data Mining. Springer International Publishing, pp. 465-474. ISBN 978-3-319-51279-2

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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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