Application of data mining and machine learning approaches in the prediction of heart disease – A literature survey

Abstract

Heart disease remains a leading cause of mortality worldwide, emphasizing the urgent need for effective classification and prediction methodologies. This literature review explores various data mining and machine learning approaches utilized in the classification and prediction of heart disease. We systematically analyze a diverse range of techniques, including decision trees, support vector machines, artificial neural networks, and ensemble methods, highlighting their strengths and limitations. The review further examines pre-processing methods, feature selection, and extraction techniques that significantly impact model performance. Additionally, we discuss the integration of hybrid approaches and deep learning methods, showcasing their potential to enhance predictive accuracy. Recent advancements in data handling and algorithmic efficiency are also highlighted, demonstrating the promising role of machine learning in addressing the complexities of heart disease diagnosis. This review aims to provide a comprehensive understanding of current trends and future directions in heart disease classification and prediction, paving the way for improved diagnostic tools and health outcomes.