A novel approach for cardiovascular disease prediction using machine learning algorithms

Abstract For the past few decades, cardiovascular disease has shown a binding impact on the country's mortality rate. The prediction of cardiovascular disease is more challenging during the process of clinical data analysis. The emergence of Machine Learning approaches paved the way to predict the disease and determining the consequences of the disease in the earlier stage to help the physicians during complex decision‐making. This work adopts k‐Nearest Neighbor as baseline classifier and ensemble X‐boost, Adaboost, and Random subspace classifier model to predict heart disease and predict the features of cardiovascular disease using Linear Support Vector Feature Measure (). This model considers the diverse combination of features to make the better classification process. The model shows superior performance with precision via Clinical Decision Support System. The factors that influence the cardiovascular disease need to predict, and better decision is taken during the critical condition. Here, the online available University of California Irvine (UCI) Machine Learning dataset is used for training and testing where 80% data is considered for training and 20% considered for testing purpose. The simulation is done in MATLAB 2020b simulation environment, and the outcomes are compared with various existing approaches. Here, performance metrics like accuracy, precision, F‐measure, stability rate, region of curve, and recall is measured to show the model efficiency. The prediction accuracy of the proposed model is 96% which is higher than existing approaches. The overall performance of proposed ensemble model is 96% accuracy, 97% precision, 95% sensitivity, 95% F‐measure, 93% Matthew's correlation coefficients, 4.53% False Positive Rate, 3.10% False Negative Rate, and 96% True Positive Rate, respectively.