Prediction of Heart Disease using Bayesian Network Model
Thesis
The Heart Disease according to the survey is the leading cause of death all over the world. The health sector has a lot of data, but unfortunately, these data are not well utilized. This is as a result of lack of effective analysis tools to discover salient trends in data. Data Mining can help to retrieve valuable knowledge from available data. It helps to train model to predict patients’ health which will be faster compared to clinical experimentation. A lot of research has been carried out using the Cleveland heart datasets. Different Implementation of machine learning algorithms such as K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Naïve Bayes, etc. have been applied but there has been limit to modeling using Bayesian Belief Network. This research tackles this drawback. This research applied Bayesian network (BN) modeling to discover the relationship between the 14 relevant attributes of the Cleveland heart data set from University of California, Irvine. The BN produce a reliable and transparent graphical representation between the attributes with the ability to predict new scenarios which makes it an artificial intelligent tool. The model has an accuracy of 85%, precision of 86%, recall of 85% and f1-score of 85%. It was concluded that the model outperformed Naïve Bayes classifier which have accuracy of 80%, precision of 81%, recall of 80% and f1-score of 80%.