RJSET Volume 9 Issue 2 [Year 2019] ISSN 2454-3195 (online) RESEARCH JOURNAL OF SCIENCE ENGINEERING AND TECHNOLOGY www. rjset.com Page 18 A Review on Heart Disease Prediction Using Hybrid Classifier Mudassir Ahmad Dr. Sonia Vatta M.Tech, CSE HOD, CSE ABSTRACT Heart diseases are one of the major causes of death nowadays. Smoking, consumption of alcohol in large quantity, cholesterol, and pulse rate are the reason of heart diseases. This work is based on heart disease prediction using data mining. The prediction analysis is the technique of data mining which can predict further possibilities based in the current information. The data set has 13 numbers of attributes for the heart disease prediction. In the previous research work, the SVM classifier is applied for the heart disease prediction. Due to large number of attributes in the dataset, SVM classifier is not able to classify all the attributes due to which accuracy is low. In this work, the hybrid classifier will be designed based on the random forest and decision tree classifier. The Random Forest Cassifier will extract features of the dataset and Decision Tree Classifier will generate the final predicted results. The proposed and existing techniques will be implemented analyzed in terms of accuracy, precision, recall and execution time. Keywords: Heart Disease, Data Mining, Decision Tree, Random Forest, Hybrid Classifier Introduction Heart Diseases Prediction The heart is the operating system of human body, if it will not function properly then it will directly affects the functioning of the other body parts. Some of the major factors leads to the heart diseases are family history, high blood pressure, high rate of cholesterol, age, poor diet and many more. The stretching of blood vessels will increases the blood pressure which will further cause the cardiac rest. Smoking is one of the major causes of heart diseases; almost 40% of the population is dying because of this. Because it limits the supply of oxygen in our body and prevents the proper flow of blood and tightens the blood vessels. Different types of data mining techniques are employed for the prediction of data mining like Naïve Bayes, KNN algorithm, Decision tree, Neural Network. In KNN algorithm K user defined value is used to find the values of factors that lead to heart diseases. Decision tree is used to deploy classified report on the heart suffering patients. The Naïve Bayes is employed to predict the probability of the heart diseases. Last but not the least, the neural networks are used to minimize the errors occurred at the time of prediction. By using all these techniques, the records are classified as well as maintained regularly [5]. The activity of every patient is properly checked, if there is any change, and then the level of risk is informed to the patients. With the help of all these classifiers, the doctors are able to predict the heart diseases at the very initial stage.