Predictive Modelling of Diseases Based on a Network and Machine Learning Approach

Abstract

Chronic diseases have become the first prioritized concern of the health industry, so understanding the disease progression is necessary for predicting, planning and preparing resources to prevent and cure the diseases most effectively. Basing on patients’ medical history, this research analyzes and builds disease network to exploit hidden information showing the disease relations and progressions, applys machine learning models to assess the risks of morbidity and predicts the risk of contracting cardiovascular diseases (CVD) in patients with type-2 diabetes (T2D). The research data includes 249,809 medical histories of 65,337 patients in Ho Chi Minh City, Vietnam. The accuracies of the four prediction models (SVM, DT, RF and KNN) range from 78% to 80%. The predicted data can be used promisingly as a reference for medical specialists to provide effective healthcare guidance to patients as well as for healthcare service providers to use their data effectively and enhance their service quality.