Regression Based Model for Prediction of Heart Disease Recumbent
M.Diviya1, G.Malathi2, A.Karmel3
1M.Diviya*,School of Computing Science and Engineering, VIT Chennai Campus, Chennai, India.
2Dr.G.Malathi, School of Computing Science and Engineering, VIT Chennai Campus, Chennai, India.
3Dr.A.KArmel, School of Computing Science and Engineering, VIT Chennai Campus, Chennai, India.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 6639-6642| Volume-8 Issue-4, November 2019. | Retrieval Number: D8888118419/2019©BEIESP | DOI: 10.35940/ijrte.D8888.118419

Open Access | Ethics and Policies | Cite  | Mendeley | Indexing and Abstracting
© 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: Supervised Learning, a novel method that figures out how to anticipate the resultant of an input-output pair by inducting data under series of training and testing functions. Regression model is a sub classification of Supervised Machine Learning. In this paper various Regression models such as Logistic Regression, SVM, KNN, Naive Bayes and Random forest have been applied on Heart Disease dataset. The anticipated outcomes draw the deduction on the level of patients inclined to coronary illness dependent on the traits and qualities. In reference to the applied calculations both KNN and Random Forest beats the other relapse calculation with a precision of 88.52%.
Keywords: Supervised Learning, Logistic Regression, SVM, KNN, Naive Bayes and Random forest..
Scope of the Article: Artificial Intelligence and Machine Learning.