Heart Disease Prediction using Machine Learning
N. Saranya1, P. Kaviyarasu2, A. Keerthana3, C. Oveya4

1Mrs. N. Saranya , Assistant Professor, Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore.
2P. Kaviyarasu, UG Student, Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore.
3A. Keerthana, UG Student , Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore.
4C. Oveya, UG Student, Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore.

Manuscript received on April 02, 2020. | Revised Manuscript received on April 15, 2020. | Manuscript published on May 30, 2020. | PP: 700-704 | Volume-9 Issue-1, May 2020. | Retrieval Number: F9780038620/2020©BEIESP | DOI: 10.35940/ijrte.F9780.059120
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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: Deriving the methodologies to detect heart issues at an earlier stage and intimating the patient to improve their health. To resolve this problem, we will use Machine Learning techniques to predict the incidence at an earlier stage. We have a tendency to use sure parameters like age, sex, height, weight, case history, smoking and alcohol consumption and test like pressure ,cholesterol, diabetes, ECG, ECHO for prediction. In machine learning there are many algorithms which will be used to solve this issue. The algorithms include K-Nearest Neighbour, Support vector classifier, decision tree classifier, logistic regression and Random Forest classifier. Using these parameters and algorithms we need to predict whether or not the patient has heart disease or not and recommend the patient to improve his/her health. 
Keywords: Classifier, Heart disease, K nearest neighbor , Prediction, Random forest
Scope of the Article: Machine Learning