Heart Disease Prediction based on Machine learning Technique

Any condition that poses a significant risk to one’s life and affects the cardiovascular system is referred to as ‘‘cardiovascular disease Because of the potentially lethal nature of heart conditions, scientists are working to develop intelligent systems that can accurately diagnose these conditions using electronic health data and machine learning algorithms. These systems will be able to draw their information from electronic health records. In this article, to provide a number of different machine learning algorithms for the purpose of predicting cardiovascular disease based on an examination of the demographic and clinical data of patients. In the research article, a total of four distinct categorization approaches were discussed for the purpose of creating predictions. These included the Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes models (NB). The data were cleaned up, and then features were decided upon, before the models were constructed. When ranking the performance of the models, numerous other criteria, such as the F1-score, accuracy, precision, and recall, were utilized. The Logistic Regression Classifier was successful in achieving an overall score of 91.8%.