Exploring the Efficiency of Various Supervised Machine Learning Techniques to Predict the Heart Disease using Risk Factors
Jaishri Wankhede1, Palaniappan Sambandam2, S. Magesh Kumar3

1Jaishri Wankhede, Research Scholar, Department of CSE, Saveetha School of Engineering Simats, (Tamil Nadu), India.

2Dr. Palaniappan Sambandam, Associate Professor, Department of CSE, KCG College of Technology, Chennai (Tamil Nadu), India.

3Dr. S. Magesh Kumar, Associate Professor, Department of CSE, Saveetha School of Engineering Simats, (Tamil Nadu), India.

Manuscript received on 25 November 2019 | Revised Manuscript received on 06 December 2019 | Manuscript Published on 14 December 2019 | PP: 309-312 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10631191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1063.1191S19

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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: Data Science in healthcare is a innovative and capable for industry implementing the data science applications. Data analytics is recent science in to discover the medical data set to explore and discover the disease. It’s a beginning attempt to identify the disease with the help of large amount of medical dataset. Using this data science methodology, it makes the user to find their disease without the help of health care centres. Healthcare and data science are often linked through finances as the industry attempts to reduce its expenses with the help of large amounts of data. Data science and medicine are rapidly developing, and it is important that they advance together. Health care information is very effective in the society. In a human life day to day heart disease had increased. Based on the heart disease to monitor different factors in human body to analyse and prevent the heart disease. To classify the factors using the machine learning algorithms and to predict the disease is major part. Major part of involves machine level based supervised learning algorithm such as SVM, Naviebayes, Decision Trees and Random forest.

Keywords: Heart Disease Prediction, Machine Learning, LSVC, SVM.
Scope of the Article: Machine Learning