AI Analysis of Cardiac Ailments: A Comprehensive Study

This paper presents a comprehensive study on using artificial intelligence (AI) techniques to analyze and detect cardiac ailments from clinical and physiological data. We investigate classical machine learning and deep learning approaches for multiple diagnostic tasks: (1) detection of arrhythmias from ECG signals, (2) prediction of coronary artery disease (CAD) risk using clinical features, and (3) detection of heart failure from imaging and structured data. We describe dataset selection and preprocessing, propose an end-to-end CNN–LSTM model for ECG classification, evaluate ensemble models for clinical-risk prediction, and benchmark performance against established baselines. Experimental results on public datasets demonstrate that AI models can achieve clinically useful performance, though careful attention to data quality, interpretability, and external validation is required. We conclude with a discussion of limitations, ethical considerations, and directions for future research Key Words: Cardiovascular diseases, myocardial infarction, heart failure, arrhythmia, coronary artery disease, hypertension, ECG, echocardiography, cardiac biomarkers, angiography, LVEF, artificial intelligence, machine learning, deep learning, neural networks, CNN, RNN, LSTM