Optimization Accuracy of Heart Disease Diagnosis using Machine Learning Approach

Heart disease is a major cause of death worldwide. About 31% of global deaths occur due to this disease. According to the WHO report, approximately 17.9 million people worldwide die each year due to this disease. Early detection of heart disease can reduce the chance of the disease progressing to a more severe stage by providing appropriate treatment. Coronary angiography is the most precise method of detecting the disease; nevertheless, it is a painful and expensive procedure, and patients do not prefer it in the early stages. As a result, a non-invasive and reliable method that can be employed as a screening tool for disease diagnosis is required. Machine learning has influenced every element of human life in the modern era, and it has a huge impact in the health care sector also. Machine learning (ML) based decision support systems can be used to diagnose heart disease by evaluating the clinical parameters of a patient. Various ML techniques have been investigated in this paper, and a system has been designed for the diagnosis of heart disease, with an emphasis on coronary artery disease. Relevant features were selected using the Extra Tree feature selection method and classification was done by XGBoost (extreme gradient boosting), KNN (K-nearest neighbor), SVM (support vector machine), logistic regression (LR), decision tree (DT), random forest (RF) and neural network (NN) classifiers. A regularized NN model for heart disease diagnosis is also developed that has provided 84.0% accuracy and 85.0% accuracy for RF on the Kaggle dataset.