A Fast Algorithm for Heart Disease Prediction using Bayesian Network Model
This research provides an incremental improvement in heart disease prediction for medical practitioners by demonstrating a slightly higher accuracy with a Bayesian Network model.
This paper addresses the prediction of heart disease using a Bayesian Network (BN) model on the Cleveland heart dataset. The BN model achieved an accuracy of 85%, outperforming the Naive Bayes classifier which had an accuracy of 80%.
Cardiovascular disease is the number one cause of death all over the world. Data mining can help to retrieve valuable knowledge from available data from the health sector. It helps to train a model to predict patients' health which will be faster as compared to clinical experimentation. Various implementation of machine learning algorithms such as Logistic Regression, K-Nearest Neighbor, Naive Bayes (NB), Support Vector Machine, etc. have been applied on Cleveland heart datasets but there has been a limit to modeling using Bayesian Network (BN). This research applied BN modeling to discover the relationship between 14 relevant attributes of the Cleveland heart data collected from The UCI repository. The aim is to check how the dependency between attributes affects the performance of the classifier. The BN produces a reliable and transparent graphical representation between the attributes with the ability to predict new scenarios. The model has an accuracy of 85%. It was concluded that the model outperformed the NB classifier which has an accuracy of 80%.