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Volume 10
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10.3390/healthcare10061137
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Article

The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction

by
Nurul Absar
1,
Emon Kumar Das
1,
Shamsun Nahar Shoma
1,
Mayeen Uddin Khandaker
2,3,*,
Mahadi Hasan Miraz
4,
M. R. I. Faruque
5,
Nissren Tamam
6,
Abdelmoneim Sulieman
7 and
Refat Khan Pathan
8
1
Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
2
Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia
3
Department of General Educational Development, Faculty of Science and Information Technology, Daffodil International University, DIU Rd, Dhaka 1341, Bangladesh
4
Department of Business Analytics, Sunway University, Petaling Jaya 47500, Selangor, Malaysia
5
Space Science Center, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
6
Department of Physics, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
7
Department of Radiology and Medical Imaging, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
8
Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia
*
Author to whom correspondence should be addressed.
Healthcare 2022, 10(6), 1137; https://doi.org/10.3390/healthcare10061137
Submission received: 13 April 2022 / Revised: 13 June 2022 / Accepted: 14 June 2022 / Published: 18 June 2022
(This article belongs to the Special Issue Computing and Artificial Intelligence Techniques for Healthcare Applications: Second Edition)
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Abstract

The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.
Keywords: decision tree; random forest; KNN; AdaBoost; heart disease; prediction; smart system decision tree; random forest; KNN; AdaBoost; heart disease; prediction; smart system

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MDPI and ACS Style

Absar, N.; Das, E.K.; Shoma, S.N.; Khandaker, M.U.; Miraz, M.H.; Faruque, M.R.I.; Tamam, N.; Sulieman, A.; Pathan, R.K. The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction. Healthcare 2022, 10, 1137. https://doi.org/10.3390/healthcare10061137

AMA Style

Absar N, Das EK, Shoma SN, Khandaker MU, Miraz MH, Faruque MRI, Tamam N, Sulieman A, Pathan RK. The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction. Healthcare. 2022; 10(6):1137. https://doi.org/10.3390/healthcare10061137

Chicago/Turabian Style

Absar, Nurul, Emon Kumar Das, Shamsun Nahar Shoma, Mayeen Uddin Khandaker, Mahadi Hasan Miraz, M. R. I. Faruque, Nissren Tamam, Abdelmoneim Sulieman, and Refat Khan Pathan. 2022. "The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction" Healthcare 10, no. 6: 1137. https://doi.org/10.3390/healthcare10061137

APA Style

Absar, N., Das, E. K., Shoma, S. N., Khandaker, M. U., Miraz, M. H., Faruque, M. R. I., Tamam, N., Sulieman, A., & Pathan, R. K. (2022). The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction. Healthcare, 10(6), 1137. https://doi.org/10.3390/healthcare10061137

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