Heart diseases, are among the leading causes of mortality worldwide. Early prediction and prevention of heart disease can significantly reduce fatalities and improve patients' quality of life. In this study, we propose an advanced hybrid approach that combines multiple machine learning algorithms to predict the likelihood of heart disease in individuals.
            In recent times, the emergence of machine learning algorithms has shown great promise as a means to predict the risk of heart disease, including Support Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and Naïve Bayesian (NV). Our results demonstrate the effectiveness of machine learning algorithms, both individually and in combination, for heart disease diagnosis. We provide a comprehensive analysis of the strengths and weaknesses of each algorithm, as well as the ensemble models, and evaluate our approach using eight performance matrices. Our results show that the Random Forest algorithm outperforms other algorithms with an accuracy of 96%, sensitivity of 97.6%, and specificity of 94.7%. Our findings suggest, depends on the growing body of literature, the use of machine learning algorithms for heart disease diagnosis which provides valuable insights for the way for personalized and targeted interference.