Enhancing heart disease diagnosis: Leveraging classification and ensemble machine learning techniques in healthcare decision-making

Abstract

Cardiovascular disease is one of the main reasons for the demise of people in the world today, whether it is a developed country or a developing country. It is not only affecting the people living in the urban but it has also affected the people of rural areas. If we know it at the primary stage, then its side effects can be avoided by reducing the chances of heart disease. So, the correct prediction of heart disease is an imperative task to assist doctors and medical experts to take decision and make effective treatment policies to save the lives of people. In this paper, we use and combine multiple classification method of data mining and machine learning to perk up the precision of classifier. For this, we have used the ensemble machine learning method which combines multiple models into a single predictive model that utilizes the advantages of multiple base models usually called weak learners to compensate for each model’s weakness. We intend an iterative ensemble approach to integrate various low-performance classifiers to form a strong classifier with high precision. We took a dataset from the IEEE data port for its implementation which contains around 1190 instances with 11 features of heart disease. We examine on the basis of initial symptoms even the patient has heart disease or not. We explore the application of classification and ensemble machine learning techniques to augment healthcare decision-making for heart disease. By bridging the gap between data-driven insights and clinical decision-making, these techniques pave the means for a more proactive and patient-centric approach to cardiovascular health management.