Detection of Heart Disease Using Machine Learning

Heart failure is a chronic disease affecting millions worldwide. An efficient machine learning- based technique is needed to predict heart failure health status early and take necessary actions to overcome this worldwide issue. While medication is the primary treatment, exercise is increasingly recognized as an effective adjunct therapy in managing heart failure. This research project aims to develop a robust predictive model for the early detection of heart diseases by leveraging machine learning techniques, with a specific focus on the application of Support Vector Machines (SVM). The growth in technology has improved the information or data which can be extracted from a patient to help pinpoint the cause of illness. Using different number of attributes or data from the medical profile of a patient can predict the chance of a patient developing a heart condition. In simple terms, these attributes are loaded into logistic regression, Decision Tree, Random Forest, SVM, KNN and Naive bayes, that is, Machine learning (ML) algorithms for the analysis and further prediction of heart disease. There are many other techniques, methods used by other researchers. By using this method, the standards in the medical industries are elevated and rose as they can provide better diagnostics and treatment of the patient, resulting in providing an overall good quality service. This has its main focus towards: Using Data analysis, creating prediction Models to provide early detection of Heart Diseases, Also, by creating a reliable method to predict heart disease. Thus, we found that supervised machine learning algorithms can be used to make heart disease predictions with very high accuracy and excellent potential utility. Our proposed research study has significant scientific contributions to the medical community.