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. 2022 Jun 18;10(6):1137.
doi: 10.3390/healthcare10061137.

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

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 , Refat Khan Pathan  8
Affiliations

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

Nurul Absar et al. Healthcare (Basel). .

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: AdaBoost; KNN; decision tree; heart disease; prediction; random forest; smart system.

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Conflict of interest statement

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
The system architecture of the present work.
Figure 2
Figure 2
The outliers present in the Cleveland dataset.
Figure 3
Figure 3
The changes of box plot after the outlier removal using IQR in the Cleveland dataset.
Figure 4
Figure 4
The AUC curve of test data using the CHSLB datasets for the used models.
Figure 5
Figure 5
The AUC curve of test data using the Cleveland dataset for the used models.
Figure 6
Figure 6
The accuracy performance graph for the Cleveland dataset.
Figure 7
Figure 7
The accuracy performance graph for the CHSLB datasets.
Figure 8
Figure 8
Real-time web-based smart system for heart disease prediction.
See this image and copyright information in PMC

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