The Prediction of Heart Disease

Increasing prevalence of heart disease is a significant challenge of public health and emphasizes the need for early detection and effective intervention. Traditional diagnostic techniques such as electrocardiograms (ECG), echocardiography and blood pressure monitoring often cannot detect heart disease because they rely on subjective interpretation and may not identify basic risk factors before symptoms. As a result, many patients are diagnosed only after serious complications, which reduces the efficacy of preventive measures. This article examines the application of different ml algorithms in the prediction of heart disease and compares their efficacy with traditional diagnostic approaches. By displaying key metrics, such as accuracy, accuracy, memory and F1-Score, this research emphasizes the benefits of ML-based models in improving early diagnosis and risk stratification. The study also discusses challenges such as data quality, interpretability of ML models and the deployment of AI -based diagnostics in clinical practice.