Data mining techniques for classification of childhood obesity among year 6 school children

• Main Authors: Engku Fadzli Hasan, Syed Abdullah, Saidah, Manan, Aryati, Ahmad, Sharifah Wajihah Wafa, Syed Saadun Tarek Wafa, Mohd Razif, Shahril, Nurzaime, Zulaily, Rahman, Mohd Amin, Amran, Ahmed
• Format: Conference or Workshop Item
• Language: English
• Published: 2017
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software
• Online Access: http://eprints.unisza.edu.my/1685/1/FH03-FIK-17-08104.jpg; http://eprints.unisza.edu.my/1685/
• Institution: Universiti Sultan Zainal Abidin

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.