INTERNATIONAL RESEARCH JOURNAL OF SCIENCE ENGINEERING AND TECHNOLOGY

( Online- ISSN 2454 -3195 ) New DOI : 10.32804/RJSET

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SURVEY OF HEART DISEASE PREDICTION TECHNIQUES

    2 Author(s):  MUDASSIR AHMAD,DR. SONIA VATTA

Vol -  9, Issue- 1 ,         Page(s) : 30 - 39  (2019 ) DOI : https://doi.org/10.32804/RJSET

Abstract

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 arrest. Healthcare industry has huge amount of data that contains hidden information. This information supports decision making process on related area. Huge amount of patient related data is maintained on monthly basis. The stored data can be useful for source of predicting the occurrence of future disease. The successful application of data mining in highly visible fields like e-business, marketing and retail has led to its application in other industries and sectors. Among these sectors just discovering is healthcare. The Healthcare industry is generally “information rich”, but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining modelling techniques can help remedy this situation. 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 leads 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. 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. Some of the data mining and machine learning techniques are used to predict the heart disease. In this review paper, we discussed various approaches of data mining which are useful in predicting the heart disease. Data analytics is useful for prediction from more information and it helps medical centre to predict of various disease. The aim of this work is to summarize some of the current research on predicting heart diseases using data mining techniques, analyse the various combinations of mining algorithms used and conclude which technique(s) are effective and efficient. Also, some future directions on prediction systems have been addressed.

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