Utilizing Longitudinal Data to Build Decision Trees for Profile Building and Predicting Eating Behavior

In this paper a framework for warning people when they are at risk of unhealthy eating is presented. Data is collected trough a mo- bile application called “ThinkSlim” which was developed for the purpose of studying eating behavior using Ecological Momentary Assessment (EMA) principles. Data is converted in order to allow early prediction of healthy and unhealthy eating events and a decision tree algorithm taking into account the longitudinal structure of the dataset is utilized to predict healthy versus unhealthy eating events. Rules that are derived from this decision tree are used to cluster users to groups based on the rule triggering frequen- cies. Groups created are used for providing users with semi-tailored feedback and are analyzed providing useful insights regarding the conditions that lead to unhealthy eating among different participants allowing for building different eating profiles.