A TWO-TIER MACHINE LEARNING FRAMEWORK FOR RISK ASSESSMENT IN DRIVERS WITH CARDIOVASCULAR DISORDERS

Abstract

This work proposes a scheme based on a two-tier machine learning (ML) framework for the initial screening of commercial drivers with cardiovascular disorders prior to the actual driving assessment. First, the proposed framework aims to provide primary health care to cardiac drivers in resource-constrained scenarios such as bus terminals with the help of paramedical staff. The prediction of cardiovascular disease (CVD) in drivers is done using a variety of ML approaches, including Support Vector Machines (SVMs), Random Forests (RFs), Logistic Regression (LR), K-Nearest Neighbor (KNN), Decision Trees (DT), Naive Bayes (NB), and XG-Boost (XGB). The K-fold cross-validation technique also tests the model’s ability to predict CVD. Second, a no-drive alert will be provided whenever the model predicts heart disease, and a comma-separated value (CSV) file stores the predicted abnormal parameters. An email-based data communication has been set up to transfer the CSV file. A MySQL database has been created to store the abnormal data received in hospitals which will help cardiologists with the proper diagnosis. This internet of medical things (IoMT) process will enable divers to come to the hospital for medication only when advised by a cardiologist, thereby reducing the burden of routine hospital visits. The Cleveland database of the UCI ML repository, a multivariate CVD dataset that contains 14 features from 303 people, is utilized to test the performance of the proposed model. Also, the proposed model performance is evaluated using two more publicly available heart disease datasets, i.e., the MIT-BIH arrhythmia dataset and the CVD dataset. The XGB, KNN, and RF ML techniques outperform state-of-the-art methods with performance accuracies of 88.53%, 91.8%, and 93.44%, respectively, for the Cleveland database; performance accuracies of 99.20%, 98.82%, and 99.08% for the MIT-BIH arrhythmia dataset; and performance accuracies of 73.29%, 69.48%, and 71.74% for the CVD dataset. Furthermore, the results showed comparable performance to the rest of the ML techniques. Early detection of CVD and consultation with specialist doctors are essential before it reaches a seriousness that can save drivers from vehicular accidents while seeking health care.