Kasim, Sazzli and Rudin, Putri Nur Fatin Amir and Malek, Sorayya and Ibrahim, Khairul Shafiq and Ahmad, Wan Azman Wan and Fong, Alan Yean Yip and Lin, Wan Yin and Aziz, Firdaus and Ibrahim, Nurulain (2024) Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI). Scientific Reports, 14 (1). p. 12378. ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-024-61151-x.
Full text not available from this repository.Abstract
The accurate prediction of in-hospital mortality in Asian women after ST-Elevation Myocardial Infarction (STEMI) remains a crucial issue in medical research. Existing models frequently neglect this demographic's particular attributes, resulting in poor treatment outcomes. This study aims to improve the prediction of in-hospital mortality in multi-ethnic Asian women with STEMI by employing both base and ensemble machine learning (ML) models. We centred on the development of demographic-specific models using data from the Malaysian National Cardiovascular Disease Database spanning 2006 to 2016. Through a careful iterative feature selection approach that included feature importance and sequential backward elimination, significant variables such as systolic blood pressure, Killip class, fasting blood glucose, beta-blockers, angiotensin-converting enzyme inhibitors (ACE), and oral hypoglycemic medications were identified. The findings of our study revealed that ML models with selected features outperformed the conventional Thrombolysis in Myocardial Infarction (TIMI) Risk score, with area under the curve (AUC) ranging from 0.60 to 0.93 versus TIMI's AUC of 0.81. Remarkably, our best-performing ensemble ML model was surpassed by the base ML model, support vector machine (SVM) Linear with SVM selected features (AUC: 0.93, CI: 0.89-0.98 versus AUC: 0.91, CI: 0.87-0.96). Furthermore, the women-specific model outperformed a non-gender-specific STEMI model (AUC: 0.92, CI: 0.87-0.97). Our findings demonstrate the value of women-specific ML models over standard approaches, emphasizing the importance of continued testing and validation to improve clinical care for women with STEMI.
| Item Type: | Article |
|---|---|
| Funders: | Kementerian Sains, Teknologi dan Inovasi, Malaysia (TDF03211036) |
| Uncontrolled Keywords: | Acute coronary syndrome; TIMI risk score; 30-day mortality; gender; models; classification; disease; performance; registry; Malaysia |
| Subjects: | Q Science > QH Natural history R Medicine |
| Divisions: | Faculty of Science > Institute of Biological Sciences Universiti Malaya Medical Centre (UMMC) |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 26 Sep 2024 03:04 |
| Last Modified: | 26 Sep 2024 03:04 |
| URI: | http://eprints.um.edu.my/id/eprint/45211 |
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