Research Paper on Heart Disease Prediction using Genetic Algorithm with Rule Based Classifier in Data Mining
Classification is a process that has been used for the extraction of various hidden features from dataset as well as used for making various decisions. Due to advancement in technology, the medical field has been interrelated to data mining and machine learning approaches for the prediction of various diseases on the basis of values of tests. In this paper a novel approach has been developed that is a combination of genetic algorithm and decision table naïve bayes classifier which is used for the prediction of heart disease based on set of rules. This approach has been compared with support vector machine classifier using dataset of 303 patients. Our results show that decision table naïve bayes is able to classify more correctly with an accuracy of 86.4% than support vector classifier with an accuracy of 83.4%.
Data mining, Heart disease, Genetic algorithm, Rule based classifier, Support vector machine, Decision table naive bayes.