Title: Implementation of Computer-Aided Medical Decision Support System For The Prediction and Classification Of Heart Disease Using Machine Learning
Authors: Mr. B.Vinod and Dr.G.Geetha
Volume: 9
Issue: 6
Pages: 135-139
Publication Date: 2025/06/28
Abstract:
In this paper, an AI-based computer-aided heart disease diagnosis decision support system has been proposed using clinical data, patient information, and electrocardiogram (ECG) data. The proposed system includes three modules: an ECG processor module that allows cardiologists to process and analyze the different waveforms, a machine learning-based heart disease prediction module based on patient information and clinical data, and a deep learning-based 18 heart conditions multiclass classification module using 12- lead ECG data. A user-friendly user interface has also been developed for ease of use of the proposed techniques. Results: The heart disease prediction module was found to be 100% accurate in predicting heart disease based on clinical and patient information, and the multiclass classification module was 93.27% accurate, on average, in classifying heart conditions based on a 12- lead ECG signal. The ECG processor also provides quick diagnosis by analyzing important ECG waveforms and segments. Conclusion: The proposed system may have the potential for facilitating heart disease diagnosis. The proposed method allows physicians to analyze and predict heart disease easily and early, based on the available resource, improving diagnosis accuracy and treatment planning