An Intelligent Machine Learning Approaches for Predicting Coronary Artery Disease (original) (raw)
Coronary Artery Disease (CAD) destroys the internal layer of the artery. Consequently, this destruction leads the fatty sediments to escalate the injury. CAD is one of the common significant reasons of death all around the world, thus early detection of CAD will facilitate scale back these rates. The medical industries gather a large number of facts which include some unknown data to make the choice effective. They also use some excellent data processing methods. The CAD prediction indicates the probability of patients getting artery disease. In this research, we propose various Machine Learning (ML) methods to predict the CAD with the help of historical data. These ML methods enable the system to learn over several datasets to acknowledge valuable understanding. The programmable capability of ML in examining, interpreting, and processing data-set is beneficial to decision-makers in the medical field. This method uses 10 medical parameters to forecast artery disease which is obtained from KEEL (Knowledge Extraction based on Evolutionary Learning). An experiment is performed with algorithms like Naive Bayes, Decision Tree, Neural Network (MLP Classifier), Logistic Regression, and Random Forest with necessary performance metrics like accuracy, precision, recall.
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