STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews: A Journal of Health Professions Review Article

AI-Powered ECG Prediction System for Detecting Cardiovascular Disease

  1. Mohammad N. Alam Department of CSE, Guru Kashi University, Bathinda
  2. Vijay Laxmi Department of Computer Applications, Guru Kashi University, Bathinda
  3. Baljinder Kaur Department of Computer Applications, Guru Kashi University, Bathinda

Abstract

The proposed AI-powered CardioSmart Analyzer, an electrocardiogram (ECG) prediction system, presents an innovative and scientifically rigorous approach to the real-time automated analysis of ECG signals for diagnosing various heart conditions. This research focused on building a predictive model to identify cardiovascular diseases (CVD) using ECG data. A dataset comprising 2,840 12-lead ECG recordings was gathered from medical facilities in Gazipur, Bangladesh, over the period from June to August 2024. The analysis revealed 68 unique diagnostic categories based on the interpretations of the patients’ ECG reports. To carry out the classification and prediction of these cardiovascular conditions, a robust random forest algorithm was implemented. This machine learning model proved to be highly effective, yielding outstanding performance results. In both binary and multi-class classification tasks, the algorithm achieved a remarkable accuracy rate of 100%. The success of this approach highlights its potential application in clinical settings, where automated ECG interpretation could assist healthcare professionals in the early and accurate diagnosis of a wide range of heart-related conditions. Overall, AI-driven ECG-based prediction model exhibited excellent performance in detecting common CVD conditions.

Keywords

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