Heart disease diagnosis and prediction based on hybrid machine learning model
Author(s): Viswanatha Reddy Allugunti
Abstract: Heart disease is responsible for a considerable percentage of deaths that occur all over the globe, and it has developed into a major health risk for many individuals. The detection of cardiovascular diseases such as heart attacks, myocardial infarction diseases, and others like these is a crucial problem that must be met by the regular clinical data analysis. Early prevalence of heart failure may save the lives of many people. The application of machine learning (ML) can provide an efficient answer to the problem of making decisions and accurate forecasts. The application of machine learning strategies is making significant headway in the medical sector. In the work that has been presented, a unique technique to machine learning is proposed for the purpose of predicting cardiac disease. The PhysioNet Dataset was utilised for the study that was proposed, and data mining algorithms like regression and classification were utilised. Support Vector Machine, Decision Tree and Random Forest are both the machine learning approaches that are utilised here. The cutting-edge strategy for the machine learning model has been devised. Support Vector Machine, Random Forest, Decision Tree, and the Hybrid model (Hybrid of SVM, RF and DT) are the four types of machine learning algorithms that are utilised in the implementation process. The accuracy level of the heart disease prediction model using the hybrid model was found to be 88.7 percent based on the results of the experiments. The user's input parameter will be utilised to predict heart illness, which will be done with a model that is a hybrid of Decision Tree and Random Forest. The interface is built to acquire the user's input parameter.