Chronic Disease Diagnoses Using Behavioral Data

TL;DR

This study demonstrates that behavioral data collected from individuals can be effectively used with machine learning models to diagnose three common chronic diseases early, without relying on traditional medical data.

Contribution

The paper introduces a novel approach for early disease detection using behavioral data and machine learning, bypassing the need for clinical medical data.

Findings

Achieved over 80% accuracy in diagnosing diabetes and hypertension.

Identified key behavioral features influencing disease predictions.

Validated that behavioral data can substitute medical data for early diagnosis.

Abstract

Early detection of chronic diseases is beneficial to healthcare by providing a golden opportunity for timely interventions. Although numerous prior studies have successfully used machine learning (ML) models for disease diagnoses, they highly rely on medical data, which are scarce for most patients in the early stage of the chronic diseases. In this paper, we aim to diagnose hyperglycemia (diabetes), hyperlipidemia, and hypertension (collectively known as 3H) using own collected behavioral data, thus, enable the early detection of 3H without using medical data collected in clinical settings. Specifically, we collected daily behavioral data from 629 participants over a 3-month study period, and trained various ML models after data preprocessing. Experimental results show that only using the participants' uploaded behavioral data, we can achieve accurate 3H diagnoses: 80.2\%, 71.3\%, and 81.2\% for diabetes, hyperlipidemia, and hypertension, respectively. Furthermore, we conduct Shapley analysis on the trained models to identify the most influential features for each type of diseases. The identified influential features are consistent with those reported in the literature.