Comparative Analysis of Prediction Coronary Heart Diseases using different Data Mining Models

Abstract

Different types of algorithm are coupled with data mining models which has helped in civilizing the performance in medical domain. There are different types of diseases effected a human body, heart diseases is one of them and itcentral aspect of medical mining where the researchers are upgrading day to day and improving accuracy by using different algorithms. BasicallyCAD (Cardiovascular Disease) is the leadingdiseases in all over the world. WHO estimated that humanity rate cause by heart diseases will gather 23 million cases by 2030. This paperpresent the general machine learning model like K Neighbors Classifier, Logistic Regression Model, Gaussian Naive Bayes Model, Decision Tree Model, Random Forest Classifier Model for prediction of heart disease and compare all used algorithmsaccording to their accuracy, sensitivity and specificity.All the measurements used 10 fold cross validation.