Machine Learning and Deep Neural Network Techniques for Heart Disease Prediction

Abstract

Heart plays a crucial role in all forms of life. Heart-related disorders demand higher precision, consistency, and accuracy in diagnosis and prognosis because even a small mistake might lead to death. Heart-related deaths are common, and the number of these deaths is rising rapidly day by day. Heart disease (HD) prediction with an acceptable level of accuracy is attainable by using cutting-edge machine learning (ML) and deep learning (DL) algorithms. Making an accurate model using these algorithms can predict and categorize cardiovascular illness with high accuracy and reduce medical testing and human intervention. In this study an assessment between ML and DL was carried out to improve classification models for heart disease prediction based on related performance metrics (Accuracy, Precision, Recall, F-1 score, and AUC curve) using a benchmark dataset from UCI machine learning databases of heart disease. which consists of 14 different heart disease-related features. Extreme Gradient Gradient Boosting (XGBoost), Ada Boost, Light Gradient Boosting Machine, CatBoost, Gradient Boosting, Random Forest, Ridge, Decision Tree, Logistic Regression, K Neighbors, SVM-Linear Kernel, Naive Bayes, and deep neural networks, DNN3(3-layer network) and DNN4(4-layer network) are just a few of the classification models that are successfully used in this work for classification tasks. The highest classification accuracy was attained with the Extreme Gradient Boosting classifier (81.10%) (among the machine learning classifiers). The three layer deep neural network (DNN3) among deep learning approaches has provided the best accuracy of 85.41% when using selected features as input. The gathered results showed that deep neural networks outperformed machine learning techniques.