Analysis of Weighted Associative Classifier, Naïve Bayesian and Decision Tree Classifierson Heart Disease

Abstract

The healthcare system collects large amounts of healthcare data which unfortunately, are not mined to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Data mining techniques can help these conditions. Nowadays, the diagnosis of diseases is a vital and intricate job in medicine. Medical diagnosis is regarded as an important yet complicated task that needs to be executed accurately and efficiently. An automatic medical diagnosis system would probably be exceedingly beneficial by bringing all of them together. This paper presents a heart disease prediction models that can assist professionals in predicting heart disease status based on the clinical data of patients from UCI data set. It analyzes of some data mining algorithms such as Weighted Associative Classifier (WAC), Naïve Bayesian and Decision Tree Classifiers for making decision of the conditions of heart disease. It enable significant knowledge, e.g patterns, relationships between medical factors related to heart disease. This paper shows the prediction accuracy result of three classifiers and which classifier are generate more accurate result than other classifiers and compares the generated accuracies by using holdout method. Data mining enable the health sector to predict patterns in the dataset.