Prediction of Early Dropouts in Patient Remote Monitoring Programs

Abstract

The analysis of medical data is a significant opportunity worldwide for national health systems to reduce costs and at the same time improve healthcare. The utilization of these technologies is done in the context of monitoring health issues, counting health goals, as well as for recording medical data. In such a context, early detection of users at risk of lower compliance rates and patterns of use of a health monitoring application suggesting a risk of abandonment is an invaluable opportunity to implement tailored intervention strategies aimed at recovering and avoiding abandonment thoughts. This study aims to identify patterns of early dropout in users of an application for mobile intervention, having access to a database of users who have experienced the impact of a digital monitoring application to improve their quality of life for at least 6 months. At the experimental stage, many different approaches for early dropout prediction were implemented with a different set of features. Specifically, the current study proposes a methodology using the Neighborhood Cleaning Rule and a specific classification algorithm based on the Stacked Generalization learning method to predict the early abandonment of users of the health monitoring application. The results showed that the proposed algorithm was able to predict the early dropout of users from the application with an accuracy of 97.6%, making it reliable enough to be used as an early warning system.