International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 09 | Sep 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2141 Role of Different Data Mining Techniques for Predicting Heart Disease Jyoti Thakur 1 , Er. Munish Katoch 2 1 Student, Dept of Computer Science Engineering, Sri Sai University Palampur (H.P) India 2 Assitant Professor, Dept. of Computer Science and Engineering, Sri Sai University Palampur (H.P) India -----------------------------------------------------------------------***------------------------------------------------------------------------ Abstract – Heart is the important organ of human body. Changes in environmental conditions and lifestyle of people give rise to various diseases in humans related to heart and millions of people every year die because of heart problems. However several techniques have been suggested by various researchers in biomedical field to design the early prediction system for the heart related problems. The artificial neural networks (ANN), Genetic algorithm (GA), K-nearest neighbor (KNN) and many other classifiers of data mining were used to design the system for heart disease prediction. In this paper a detailed analysis and comparison of the various techniques used for the cardiovascular diseases (CVD) early prediction is done. Keywords: Heart Disease Prediction, Data mining techniques, ANN, KNN, GA,CVD etc… 1. INTRODUCTION The cardiovascular diseases are regarded as the high- ranking diseases and cause the millions of deaths every year all over the world. With the changing environment, and the growing old of population, these diseases will cause various difficulties for the human beings in following years [1]. Heart disease is the most common among all diseases which results in various disorders affecting the heart as well as blood vessels [2]. In traditional medical decision systems it was not possible to provide the intelligent decisions prior to the knowledge about the disease but now forecast of the diseases related to heart is possible and it is easy for the medical experts to give better and smart decisions. As per the report from World Health Organization (WHO), heart disease is the reason for 12 million deaths yearly, all over the world. 17.3 million Humans died in the year 2008, because of Heart problems. And WHO has made a prediction according to which around 23.6 million people may die because of heart problems [3]. Also the data mining schemes are used to extract the data from reports and results verify the affects of disease. This data mining plays a significant role in predicting the diseases in biomedical field. Nowadays a patient generally suffers from various diseases under the same category so it becomes quite uneasy for the doctor to diagnose the disease. However the prediction about the diseases can be made by using the Data Mining having intelligent algorithms from the data of patient having numerous inputs. Artificial neural network is usually used to deal with such difficult jobs. A lot of patient’s data is used to train the neural network model ; according to the past data of diseases of patients predictions about the diseases are made. The feed forward neural network is trained using back propagation. With passing time, this has establish itself as a standard scheme for the tasks related to classification and prediction about diseases in medical areas and others fields as well [4]. Many types of cardiovascular illness can be identified or diagnosed by taking into account family medical history and other variables. However, it was rather hard to predict heart disease without medical exams. The main of this paper was to diagnose various cardiovascular conditions and take any necessary precautions to avoid them at an affordable pace at an early point. In' data mining' technology, characteristics for prediction of cardiovascular disease are fed into SVM and RF.This method was used in the preliminary measurements and surveys to detectcardiovascular disease at an early point and can be totally healed by appropriate diagnostics.[5] In past some of the researchers tried to discover the optimal approach for risk prediction model. The survey and comparison is made on the basis of the researcher’s discoveries and optimal model is selected. A literature survey of the several data mining techniques was done to foresee the heart disease is done in this paper. The survey make it is easy to know about the methods nowadays used to foresee the heart related diseases with the help of data mining classification of data. 2. DATA MINING TECHNIQUES There is presence of various data mining schemes to know about the diseases related to heart. Some of the basic techniques from them are listed below [5]. 2.1 Association: It is the optimal data mining scheme among all the techniques. It is taken to predict the heart disease because it derive the relation between various features after analyzing; and helps the patient to know about the factors causing the heart disease and diagnosing the disease prior to its occurrence.