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Preliminary Analysis to Investigate Accuracy of Data Mining for Childhood Obesity and Overweight Predictions

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Childhood obesity is a very worrying global epidemic and the Malaysian children have shown alarming statistics. Therefore, obesity and overweight predictions at an early age are important. This paper presents performances of eleven data mining techniques, that are sensitivity, specificity and accuracy tested using 320 Malaysian children datasets that were collected. The data mining techniques are decision tree, Support Vector Machine, Neural Networks, Discriminant Analysis, K-means Clustering, Regression and Naϊve Bayes. The results indicated that the Classification and Regression Tree has shown high specificity in normal and obesity predictions, while the Naive Bayes has shown high sensitivity in overweight and obesity predictions. Meanwhile, other techniques have adequate or poor accuracy. Overall, the data mining techniques accuracy can be improvised. Previous studies also indicated that the data mining techniques have limited prediction accuracy. Therefore, based on analysis, the data mining techniques can be enhanced to address the issue of low prediction accuracy for childhood obesity and overweight predictions.
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Keywords: Artificial Intelligence; Childhood Obesity; Data Mining; Overweight Prediction

Document Type: Research Article

Affiliations: 1: Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 31750, Perak, Malaysia 2: School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia 3: College of Computer and Information System, Department of Computer Sciences, Pricenss Noura Bint Abdulrahman University, Riyadh, Kingdom of Saudi Arabia

Publication date: October 1, 2018

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