Advancements In Heart Disease Prediction: A Machine Learning Approach For Early Detection And Risk Assessment

The primary aim of the paper is to comprehend, assess, and analyze the role, relevance, and efficiency of machine learning models in anticipating heart disease risks using clinical data. While the essentiality of heart disease risk prediction can't be emphasized more, the usage of machine learning (ML) in the identification and assessment of the effect of its multiple features on the division of patients with and without heart disease, generating a reliable clinical dataset, is equally important. The paper relies essentially on cross-sectional clinical data. The ML approach is designed potentially to strengthen various clinical features in the heart disease prognosis process. Some features turn out to be strong predictors adding potential values. The paper entails seven ML classifiers Logistic Regression, Random Forest, Decision Tree, Naive Bayes, k-nearest Neighbors, Neural Networks, and Support Vector Machine (SVM). The evaluation of the performance of each model is done based on accuracy metrics. Interestingly, the Support Vector Machine (SVM) demonstrates the highest accuracy percentage i.e. 91.51%, proving its worth among the evaluated models in the realm of predictive ability. The overall findings of the research demonstrate the superiority of advanced computational methodologies in the evaluation, prediction, improvement, and management of cardiovascular risks. In other words, the high potential of the SVM model exhibits its applicability and worth in clinical settings, leading the way to further progressions in personalized medicine and healthcare.