Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Jan 25;23(3):1351.
doi: 10.3390/s23031351.

A Machine Learning-Based Applied Prediction Model for Identification of Acute Coronary Syndrome (ACS) Outcomes and Mortality in Patients during the Hospital Stay

Syed Waseem Abbas Sherazi  1 , Huilin Zheng  1 , Jong Yun Lee  1
Affiliations

A Machine Learning-Based Applied Prediction Model for Identification of Acute Coronary Syndrome (ACS) Outcomes and Mortality in Patients during the Hospital Stay

Syed Waseem Abbas Sherazi et al. Sensors (Basel). .

Abstract

Nowadays, machine learning (ML) is a revolutionary and cutting-edge technology widely used in the medical domain and health informatics in the diagnosis and prognosis of cardiovascular diseases especially. Therefore, we propose a ML-based soft-voting ensemble classifier (SVEC) for the predictive modeling of acute coronary syndrome (ACS) outcomes such as STEMI and NSTEMI, discharge reasons for the patients admitted in the hospitals, and death types for the affected patients during the hospital stay. We used the Korea Acute Myocardial Infarction Registry (KAMIR-NIH) dataset, which has 13,104 patients' data containing 551 features. After data extraction and preprocessing, we used the 125 useful features and applied the SMOTETomek hybrid sampling technique to oversample the data imbalance of minority classes. Our proposed SVEC applied three ML algorithms, such as random forest, extra tree, and the gradient-boosting machine for predictive modeling of our target variables, and compared with the performances of all base classifiers. The experiments showed that the SVEC outperformed other ML-based predictive models in accuracy (99.0733%), precision (99.0742%), recall (99.0734%), F1-score (99.9719%), and the area under the ROC curve (AUC) (99.9702%). Overall, the performance of the SVEC was better than other applied models, but the AUC was slightly lower than the extra tree classifier for the predictive modeling of ACS outcomes. The proposed predictive model outperformed other ML-based models; hence it can be used practically in hospitals for the diagnosis and prediction of heart problems so that timely detection of proper treatments can be chosen, and the occurrence of disease predicted more accurately.

Keywords: acute coronary syndrome; diagnosis and prognosis; imbalanced data; machine learning; predictive model; soft-voting ensemble classifier.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Experimental data extraction from the original KAMIR-NIH dataset.
Figure 2
Figure 2
Overall workflow of proposed predictive modeling system.
Figure 3
Figure 3
The soft-voting ensemble classifier for predictive modeling of ACS outcomes and mortality of patients.
Figure 4
Figure 4
Confusion matrix for all class labels using proposed soft-voting ensemble classifier on KAMIR-NIH in-hospital data (a) actual confusion matrix; (b) normalized confusion matrix.
See this image and copyright information in PMC

References

    1. Bhardwaj R., Nambiar A.R., Dutta D. A study of machine learning in healthcare; Proceedings of the 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC); Turin, Italy. 4–8 July 2017; pp. 236–241.
    1. Jafar A., Hameed M.T., Akram N., Waqas U., Kim H.S., Naqvi R.A. CardioNet: Automatic Semantic Segmentation to Calculate the Cardiothoracic Ratio for Cardiomegaly and Other Chest Diseases. J. Pers. Med. 2022;12:988. doi: 10.3390/jpm12060988. - DOI - PMC - PubMed
    1. Vollmer S., Mateen B.A., Bohner G., Király F.J., Ghani R., Jonsson P., Cumbers S., Jonas A., McAllister K.S., Myles P., et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ. 2020;368:16927. doi: 10.1136/bmj.l6927. - DOI - PMC - PubMed
    1. Naqvi R.A., Hussain D., Loh W.K. Artificial intelligence-based semantic segmentation of ocular regions for biometrics and healthcare applications. CMC Comput. Mater. Contin. 2021;66:715–732. doi: 10.32604/cmc.2020.013249. - DOI
    1. Abraham W.T., Fonarow G.C., Albert N.M., Stough W.G., Gheorghiade M., Greenberg B.H., OPTIMIZE-HF Investigators and Coordinators Predictors of in-hospital mortality in patients hospitalized for heart failure: Insights from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF) J. Am. Coll. Cardiol. 2008;52:347–356. doi: 10.1016/j.jacc.2008.04.028. - DOI - PubMed

LinkOut - more resources