Predictive Analysis of Heart Diseases With Machine Learning Approaches

Machine Learning (ML) is used in healthcare sectors worldwide. ML methods help in the protection of heart diseases, locomotor disorders in the medical data set. The discovery of such essential data helps researchers gain valuable insight into how to utilize their diagnosis and treatment for a particular patient. Researchers use various Machine Learning methods to examine massive amounts of complex healthcare data, which aids healthcare professionals in predicting diseases. In this research, we are using an online UCI dataset with 303 rows and 76 properties. Approximately 14 of these 76 properties are selected for testing, which is necessary to validate the performances of different methods. The isolation forest approach uses the data set’s most essential qualities and metrics to standardize the information for better precision. This analysis is based on supervised learning methods, i.e., Naive Bayes, SVM, Logistic regression, Decision Tree Classifier, Random Forest, and K- Nearest Neighbor. The experimental results demonstrate the strength of KNN with eight neighbours order to test the effectiveness, sensitivity, precision, and accuracy, F1-score; as compared to other methods, i.e., Naive Bayes, SVM (Linear Kernel), Decision Tree Classifier with 4 or 18 features, and Random Forest classifiers.