Enhancing Cardiovascular Health through AI-Driven Heart Disease Detection

Early detection of heart issues thanks to AI working hand in hand with cardiovascular health. This method reliably predicts the risk for CVDD by SVM model, which have been proven consistent classification accuracy and also can maintain all information of high dimension data. The model was trained and validated on an extensive dataset of controlling significant clinical and physiological interpatient variability. In order to ascertain those medical diagnostics, become as reliable as possible, the performance of the SVM is evaluated with sensitivity, specificity, accuracy, and some other suitable metrics. The proposed model had a prediction accuracy of (96.5%) which is significantly better than existing benchmarks reflecting a significant improvement in the quality of care. With the capability of artificial intelligence (AI) to transform medical diagnostics and improve patient outcomes and the cost of healthcare. The paper demonstrates a new standard in clinical diagnostics through their analysis to provide credibility of AI-driven tools for the prompt detection and treatment of cardiovascular sicknesses with learn curves and confusion matrices, which are comprehensively performed.