Heart Disease Prediction using Supervised Machine Learning Algorithms
Sri Sai Saran Reddy Yeturu1, Vergin Raja Sarobin M2, Jani Anbarasi3, Mohith Krishna Gunapathi4, Helen D5

1Sri Sai Saran Reddy Yeturu* ,CSE, Vellore Institute of Technology, Chennai, India.
2Vergin Raja Sarobin M, CSE, Vellore Institute of Technology, Chennai, India.
3Jani Anbarasi, CSE, Vellore Institute of Technology, Chennai, India.
4Mohith Krishna Gunapathi, CSE, Vellore Institute of Technology, Chennai, India.
5Helen D, CSE, Amet University. Chennai, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 148-151 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1381029420/2020©BEIESP | DOI: 10.35940/ijitee.D1381.029420
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Generally, the most complicated task in the healthcare field is the diagnosis of the disease itself. The diagnosis phase in disease detection is usually the most time-consuming task and is prone to most of the errors. Such complications can be effectively handled if the disease detection process is well automated by incorporating effective machine learning algorithms trained with some benchmark datasets. It should also be noted that huge amounts of data that are acquired from Heart Specialization Hospitals are being wasted every year. In this paper, various classification algorithms have been used to train the machine to diagnose heart disease. By a comparative study of various learning models, we have identified the appropriate learning model for the heart disease dataset. Initially, the work will begin with an overview of various machine learning algorithms followed by the algorithmic comparison. 
Keywords: Artificial Intelligence, Machine learning, Classification algorithms, Heart Disease
Scope of the Article: Machine learning