A Network Learning Method for Functional Disability Prediction from Health Data

This contribution proposes a novel network analysis model with the goal of predicting a classification of individuals as either ‘disabled’ or ‘not-disabled’, using a dataset from the Health and Retirement Study (HRS). Our approach is based on selecting features that span health indicators and socioeconomic factors due to their pivotal roles in identifying disability. Considering the selected features, our approach computes similarities between individuals and uses this similarity to predict disability. We present a preliminary experimental evaluation of our method on the HRS dataset, where it shows an enhanced average accuracy of 62.48%.