Machine Learning Techniques for Prediction of Early Childhood Obesity.

ABSTRACT:

Objectives

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA.

Methods

Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created.

Results

Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors s

SUBMITTER: Dugan TM 

PROVIDER: S-EPMC4586339 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature