Aziida, Nanyonga and Malek, Sorayya and Aziz, Firdaus and Ibrahim, Khairul Shafiq and Kasim, Sazzli (2021) Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualisation. Sains Malaysiana, 50 (3). pp. 753-768. ISSN 0126-6039, DOI https://doi.org/10.17576/jsm-2021-5003-17.
Full text not available from this repository.Abstract
Hybrid combinations of feature selection, classification and visualisation using machine learning (ham) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. Identifying a feature selection method with a classifier algorithm that produces high performance in mortality studies is essential and has not been reported before. Feature selection methods such as Boruta, Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SI/M) and Logistic Regression (LR) were combined with RE SLM, LR, and EN classifiers for 30-day mortality prediction. ML models were constructed using 302 patients and 54 input variables from the Malaysian National Cardiovascular Disease Database. Validation of the best ML model was performed against Thrombolysis in Myocardial Infarction (TMI) using an additional dataset of 102 patients. The Self-Organising Feature Map (SOM) was used to visualise mortality-related factors post ACS. The performance of ML models using the area under the curve (AUC) ranged from 0.48 to 0.80. The best-performing model (AUC = 0.80) was a hybrid combination of the RF variable importance method, the sequential backward selection and the RF classifier using five predictors (age, triglyceride, creatinine, troponin, and total cholesterol). Comparison with T1M1 using an additional dataset resulted in the best ML model outperforming the TiM! score (AUC = 0.75 vs. AUC 0.60). The findings of this study will provide a basis for developing an online ML-based population-specific risk scoring calculator.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya (GPF013B-2018) |
| Uncontrolled Keywords: | Acute coronary syndrome; Feature selection; Hybrid model; Machine learning; Self-organising maps |
| Subjects: | Q Science > Q Science (General) Q Science > QH Natural history > QH301 Biology R Medicine > R Medicine (General) |
| Divisions: | Faculty of Science > Institute of Biological Sciences |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 19 Jul 2022 02:31 |
| Last Modified: | 19 Jul 2022 02:31 |
| URI: | http://eprints.um.edu.my/id/eprint/28045 |
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