A hybrid VGG16 and random forest model for multi-class ischemic heart disease detection via ecg image analysis

Abstract

A cardiac illnesses, mainly ischemic cardiac conditions, are a primary factor behind global fatality rates. Clinical diagnosis generally relies on manual interpretation of Electrocardiogram (ECG) signals, which is subjective and time-consuming, compounded by the challenge of limited access to raw ECG data on commercial devices. This research focuses on designing an automated diagnosis system based on ECG image analysis using the VGG16 Convolutional Neural Network (CNN) architecture through a knowledge transfer method. A total of 8,268 ECG images from 689 patients, divided into three categories Normal, Abnormal Heartbeat, and History of Myocardial Infarction (MI) were evaluated in this study. Test results demonstrated that the VGG16 architecture integrated with Random Forest produced the most optimal performance, with a test accuracy of 93.27%, an F1-Score of 93.25%, along with an Area Under the Curve (AUC) value of 0.991. This combined model successfully detected Normal images without any prediction errors. This computational image feature extraction approach has proven effective in reducing diagnostic subjectivity and holds strong potential for application as a fast and consistent clinical decision support system across various healthcare facilities. Nevertheless, multicenter validation on a more diverse population is still required to ensure the model's clinical generalizability.