Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Clicks: 31
ID: 277992
2022
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #1,676 of 1,694 articles by views in sensors

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,694 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.
Reference Key
hassan2022effectivelysensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hassan, Ch. Anwar ul;Iqbal, Jawaid;Irfan, Rizwana;Hussain, Saddam;Algarni, Abeer D.;Bukhari, Syed Sabir Hussain;Alturki, Nazik;Ullah, Syed Sajid;
Journal sensors
Year 2022
DOI
DOI not found
URL
https://www.mdpi.com/1424-8220/22/19/7227
Keywords
chemistry Chemical technology Technology electronics oceanography

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.