Heart Disease Prediction Using ML and DL Approaches

As known that heart disease (HD) is one among the most deadly diseases that hamper lives of many of the people across the globe. Life loss will be prevented when the heart disease is detected early. The cardiac hubs and hospitals are hugely depending on the ECG as a common tool to assess and diagnose the heat failure disease at early stages. Early detection of heart disease is one of the most vital issues in HCS (Health Care Services). This paper presents various Machine Learning (ML) and Deep Learning (DL) technologies based heart disease prediction systems in brief analysis. The SVM (Support Vector Machine), NB (Naïve Bayes), XGBoost, Enhanced Deep Convolutional Neutral Network (EDCNN), Deep Neutral Network (DNN) and K-Nearest Neighbour (KNN) are the used classifiers in this study. These classifiers use the data of heart disease patients from different datasets. Various performance parameters are used for the performance evaluation of individual classifiers and these are Accuracy, Precision, recall and f1-measure.