Heart Disease Prediction Methods
Mansi Gunsai1, Shreya Patel2, Kinjal V. Joshi3
1Mansi Gunsai, Department of Computer Engineering, G H Patel College of Engineering & Technology, Anand, (Gujarat), India.
2ShreyaPatel, Department of Computer Engineering, G H Patel College of Engineering & Technology, Anand, (Gujarat), India.
3Prof Kinjal Joshi, Department of Computer Engineering, G H Patel College of Engineering & Technology, Anand, (Gujarat), India.
Manuscript received on 27 April 2020 | Revised Manuscript received on 09 May 2020 | Manuscript Published on 22 May 2020 | PP: 118-121 | Volume-9 Issue-7S July 2020 | Retrieval Number: 100.1/ijitee.G10170597S20 | DOI: 10.35940/ijitee.G1017.0597S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent times, heart diseases are considered one of the deadliest causes of mortality and morbidity among the population of the world. Predicting the probability of the occurrence of cardiovascular diseases has become one of the most important objectives of the medical analysis system. The conventional methods have proved to be inefficient in prior prediction of heart diseases because of several contributing risk factors like diabetes, high blood pressure, high cholesterol, abnormal pulse rate, and others. Due to such limitations, medical practitioners rely on various modern Machine Learning and Data Mining approaches such as ANN, Naïve Bayes, SVM, etc. Such models have proved to be effective in providing accurate predictions from the huge amount of medical data available. The main aim of this paper is to analyse various machine learning approaches adopted in different research works and to deduce which techniques are most beneficial and precise.
Keywords: Heart Disease Prediction, Machine Learning, Artificial Neural Network, Naïve Bayes, Decision Tree, Genetic Algorithm.
Scope of the Article: Regression and Prediction