Comparing Some Machine Learning Models for Automatic Prediction of Patients with Cardiac Disorders

L. Yahaya¹ , B.P. Doppala²

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.7 , pp.1-9, Jul-2021

Online published on Jul 31, 2021

Copyright © L. Yahaya, B.P. Doppala . 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 :
Cardiac disorder has been among the forefront causes of sudden deaths of people of various ages worldwide. This disease has continued to rise sporadically every year, which shows that the existing predictive models are grossly insufficient. Machine learning techniques have been very effective in developing clinical decision support systems for predicting most diseases, which include those of the heart. In this paper, we presented a noble comparative approach of four machine learning models to predict heart diseases based on the UCI data, which comprises 303 instances and 14 attributes. Four machine learning models which include RF, SVM, LR, and MLP were trained and evaluated. From the experimental results, RF model appeared with the MAE value of 0.27, RMSE of 0.36, RAE of 55.30%, RRSE of 72.63% and a CC value of 0.69 which is close to the ideal value and was the highest of all. Therefore, the RF model appeared the best in predicting cardiac diseases among the compared algorithms.

Key-Words / Index Term :
Machine Learning, Models, Automatic Prediction, Prediction of Patients, Cardiac Disorders, Heart Disease

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