Coronary Heart Disease Prediction and Classification using Hybrid Machine Learning Algorithms

Abstract

Nowadays, digitalization in the healthcare organizations places great emphasis on technological advances in clinical healthcare providers. Traditional methods for measuring and evaluating outcomes for patients in forecasting and diagnosing chronic diseases are being substituted by techniques that capture the most significant insights from medical information by combining predictive modeling with a highly valuable application of machine learning. Heart disease is nowadays among the worst disorders in the world. Because the death rate from heart disease remained largely significant, more intensive efforts in preventive are required, such as enhancing the accuracy of a heart disease prediction system. Additionally, an early diagnosis supports in the appropriate diagnosis of the condition as well as the management of its symptoms. By creating forecasting analytics, Machine Learning (ML) techniques can be used to anticipate chronic diseases including kidneys and cardiac disorders. Hence, this analysis presents coronary heart disease prediction and classification utilizing Hybrid Machine Learning methods. In this approach the combination of Decision Tree (DT) and Ada Boosting algorithms is used as a hybrid ML algorithm to predict the CHD. This method's performance is determined by the performance metrics such as accuracy, True Positive Rate (TPR), and Specificity.