Published Paper Details:

Abstract: The death rate due to heart disease is one of the major global causes of increased mortality. Heart diseases are also called heart and blood vessel diseases. One of the main obstacles in clinical data analysis is to detect heart disease early. This underscores the need for effective early detection and prevention strategies these statistics continue to make cardiovascular disease (CVD) as one of the world's leading killer diseases such that its earlier detection and prevention interventions are warranted. The paper examines the use machine learning models in predicting heart disease by using support vector machines (SVM). In order to develop a model, a comprehensive dataset has been used which captures different attributes of patients including demographic information, lifestyle behaviours, and past medical history. SVMs are particularly suitable for large-scale datasets characterized by high dimensions; this approach allows identifying intricate patterns within sample sets without making any assumptions about how they may relate together or why such relationships exist between items found within your data array. Moreover, this study will critically assess and contrast ML algorithms with regards their sensitivity, specificity as well as overall accuracy. The results indicate that each algorithm exhibits distinct strengths in predicting heart disease, thereby providing valuable information to clinicians and healthcare professionals in applying strategies personalized risk assessment strategy. This research contributes to ongoing efforts to leverage ML for proactive and effective cardiovascular health management.