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

被引:15
|
作者
ul Hassan, Ch Anwar [1 ]
Iqbal, Jawaid [2 ]
Irfan, Rizwana [3 ]
Hussain, Saddam [4 ]
Algarni, Abeer D. [5 ]
Bukhari, Syed Sabir Hussain [6 ]
Alturki, Nazik [7 ]
Ullah, Syed Sajid [8 ]
机构
[1] Air Univ Islamabad, Dept Creat Technol, Islamabad 44000, Pakistan
[2] Capital Univ Sci & Technol, Dept Comp Sci, Islamabad 44000, Pakistan
[3] Univ Jeddah, Dept Comp Sci, POB 123456, Jeddah 21959, Saudi Arabia
[4] Univ Brunei Darussalam, Sch Digital Sci, Gadong, Jalan Tungku Link, BE-1410 Gadong, Brunei
[5] Princess Nourah bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Technol, POB 84428, Riyadh 11671, Saudi Arabia
[6] Sukkur IBA Univ, Dept Elect Engn, Sukkur 65200, Pakistan
[7] Princess Nourah bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Syst, POB 84428, Riyadh 11671, Saudi Arabia
[8] Univ Agder UiA, Dept Informat & Commun Technol, N-4898 Grimstad, Norway
关键词
heart disease dataset; disease prediction; supervised learning; machine learning; DIAGNOSIS; FAILURE;
D O I
10.3390/s22197227
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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%.
引用
收藏
页数:19
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