AN EFFICIENT PREDICTIVE SYSTEM FOR HEART DISEASE USING A MACHINE LEARNING TRAINED MODEL

Authors

  • Prabhavathi K
  • Dr. V Mareeswari

Keywords:

Algorithm, Heart disease, Health care, Machine learning, Prediction.

Abstract

Heart is the most important organ of a human body. Through blood, it transfers oxygen and vital nutrients to the various parts of the body and helps in the metabolic activities and it removes wastes of metabolic. Thus, even minor problems in heart can affect the whole organism. Researchers are diverting a lot of data analysis work for assisting the doctors to predict the heart problem. So, an analysis of the data related to different health problems and its functioning can help in predicting with a certain probability for the wellness of this organ. In order to assist the physicians, identification of heart disease by machine learning and data mining technique has been implemented. Heart as one of the essential organ of the human body and with its related disease such as cardiovascular diseases accounts for the death of many in our society over the last decades, and also regarded as one of the most life-threatening diseases in the world. Today healthcare industry is rich in data however poor in knowledge. There are different data mining and tools and algorithms of ML are available for extraction of knowledge from data storeand to use this knowledge for more accurate diagnosis and decision making. The main contribution of this review is tosummarize the recent research with comparative results that has been done on heart disease prediction and also make analytical conclusions. From the study, it is observed Naive Bayes with Genetic algorithm; Decision Trees and ANN techniques enhance the accuracy in predicting heart disease in different scenarios.

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Published

2021-06-30

How to Cite

K, P. ., & Mareeswari, D. V. . (2021). AN EFFICIENT PREDICTIVE SYSTEM FOR HEART DISEASE USING A MACHINE LEARNING TRAINED MODEL. The Journal of Contemporary Issues in Business and Government, 27(3), 1775–1786. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/1787

Issue

Vol. 27 No. 3 (2021): The journal of contemporary issues in business and government

Section

Articles

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