  The International Classification of Functioning, Disability, and Health for
Children and Youth (ICF-CY) is a scaffold for designating and systematizing
data on functioning and disability. It offers a standard semantic and a
theoretical foundation for the demarcation and extent of wellbeing and
infirmity. The multidimensional layout of ICF-CY comprehends a plethora of
information with about 1400 categories making it difficult to analyze. Our
research proposes a predictive model that classify self-care problems on
Self-Care Activities Dataset based on the ICF- CY. The data used in this study
resides 206 attributes of 70 children with motor and physical disability. Our
study implements, compare and analyze Random Forest, Support vector machine,
Naive Bayes, Hoeffding tree, and Lazy locally weighted learning using
two-tailed T-test at 95% confidence interval. Boruta algorithm involved in the
study minimizes the data dimensionality to advocate the minimal-optimal set of
predictors. Random forest gave the best classification accuracy of 84.75%; root
mean squared error of 0.18 and receiver operating characteristic of 0.99.
Predictive analytics can simplify the usage of ICF-CY by automating the
classification process of disability, functioning, and health.
