Hybrid approaches using decision tree, Naïve Bayes, means and euclidean distances for childhood obesity prediction

Year
2012
Type(s)
Author(s)
Adnan, M.H.M. and Husain, W. and Rashid, N.A.
Source
International Journal of Software Engineering and its Applications, 6(3): 99—106, 2012
Url
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866933741&partnerID=40&md5=ebc9f9b6c242e15a454959d7f5a5b58e
BibTeX
BibTeX

Even by using the data mining, many weaknesses still existed in childhood obesity prediction and it is still far from achieving perfect prediction. This paper studies previous steps involved in childhood obesity prediction using different data mining techniques and proposed hybrid approaches to improve the accuracy of the prediction. The steps taken in this study were a review of childhood obesity, data collections, data cleaning and preprocessing, implementation of the hybrid approach, and evaluation of the proposed approach. The hybrid approach consists of the classification and regression tree, Naïve Bayes, mean value identification and Euclidean distances classification. The results from the evaluation have shown that the proposed approach has 60% sensitivity for childhood obesity prediction and 95% sensitivity for childhood overweight prediction.