Coronary Illness Prediction and Analysis of Various Machine Learning Techniques

Banumathi P.¹ , Miraclin Joyce Pamila J.C.²

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.3 , pp.26-33, Jun-2020

Online published on Jun 30, 2020

Copyright © Banumathi P., Miraclin Joyce Pamila J.C. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The heart is an important organ for all livings, any functional problem in the heart has a direct impact on the survival of human beings. It instigates issues through the vascular to other body organs, for example, the lungs, kidney, liver, brain and so on. Consumption of junk food, alcohol, smoke, and other traditional changes affect the health of people and mainly heart in recent days. A lot of clinical information pertinent to cardiovascular ailment is produced in the clinical association and they need to be analysed critically to predict cardiovascular ailment. In the proposal, the Cleveland clinical information is examined and used to anticipate cardiovascular ailment utilizing various ML techniques. These methods use 13 clinical parameters of the patient to analyse the coronary illness. So it is highly desirable to help individuals to recognize whether they are prone to coronary illness or not. Different strategies are looked at against one another and reports performance exactness.ML techniques also compared with deep learning ANN technique. Also introduce the Random forest techniques which constructs decision trees by splitting information by subset in traditional approaches. The proposal introduces the constructions of DTs for each and every attribute separately and integrates to produce final results

Key-Words / Index Term :
Machine Learning, Artificial Neural Network, Random Forest, K nearest neighbors, Support Vector Machine, Logistic Regression, Decision Trees, XG Boost, Gaussian Na?ve Bayes

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