Heart Attack Prediction System

The Heart Attack Prediction System (HAPS), a sophisticated technology created to predict and prevent cardiovascular diseases, heart attacks in particular, which continue to be a major source of morbidity and death worldwide is introduced in this study. HAPS combines cutting-edge statistical methods and machine learning algorithms with a wide range of datasets that include important risk variables including age, blood pressure, sugar levels, cholesterol, and heart rate. This all-inclusive method uses physiological indicators in addition to age as a basic demographic element to give a full evaluation of cardiovascular health. To identify complex patterns and correlations among the input parameters, machine learning models such as neural networks, decision trees, and support vector machines are used. To determine the relevance of each parameter in the predictive model, the system goes through a rigorous training and validation process that is supplemented by feature importance analysis. In terms of sensitivity, specificity, and overall accuracy, HAPS shows encouraging results. By means of ongoing optimization and verification on various datasets, the system endeavors to enhance its prognostic efficacy and applicability. The main goal is to provide medical practitioners with a trustworthy instrument for early detection of heart attack risk, enabling proactive intervention and individualized preventative treatments. As HAPS develops, it has the potential to have a substantial influence on public health worldwide by lowering rates of heart related illness and mortality.