Machine Learning Models for Predicting Stroke Mortality in Malaysia: An Application and Comparative Analysis

Che Muhammad Nur Hidayat Che Nawi; Suhaily Mohd Hairon; Wan Nur Nafisah Wan Yahya; Wan Asyraf Wan Zaidi; Kamarul Imran Musa

doi:10.7759/cureus.50426

Machine Learning Models for Predicting Stroke Mortality in Malaysia: An Application and Comparative Analysis

Cureus. 2023 Dec 13;15(12):e50426. doi: 10.7759/cureus.50426. eCollection 2023 Dec.

Authors

Che Muhammad Nur Hidayat Che Nawi¹, Suhaily Mohd Hairon¹, Wan Nur Nafisah Wan Yahya², Wan Asyraf Wan Zaidi², Kamarul Imran Musa³

Affiliations

¹ Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, MYS.
² Department of Internal Medicine, Universiti Kebangsaan Malaysia Medical Centre (UKMMC), Kuala Lumpur, MYS.
³ Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, MYS.

Abstract

Background Stroke is a significant public health concern characterized by increasing mortality and morbidity. Accurate long-term outcome prediction for acute stroke patients, particularly stroke mortality, is vital for clinical decision-making and prognostic management. This study aimed to develop and compare various prognostic models for stroke mortality prediction. Methods In a retrospective cohort study from January 2016 to December 2021, we collected data from patients diagnosed with acute stroke from five selected hospitals. Data contained variables on demographics, comorbidities, and interventions retrieved from medical records. The cohort comprised 950 patients with 20 features. Outcomes (censored vs. death) were determined by linking data with the Malaysian National Mortality Registry. We employed three common survival modeling approaches, the Cox proportional hazard regression (Cox), support vector machine (SVM), and random survival forest (RSF), while enhancing the Cox model with Elastic Net (Cox-EN) for feature selection. Models were compared using the concordance index (C-index), time-dependent area under the curve (AUC), and discrimination index (D-index), with calibration assessed by the Brier score. Results The support vector machine (SVM) model excelled among the four, with three-month, one-year, and three-year time-dependent AUC values of 0.842, 0.846, and 0.791; a D-index of 5.31 (95% CI: 3.86, 7.30); and a C-index of 0.803 (95% CI: 0.758, 0.847). All models exhibited robust calibration, with three-month, one-year, and three-year Brier scores ranging from 0.103 to 0.220, all below 0.25. Conclusion The support vector machine (SVM) model demonstrated superior discriminative performance, suggesting its efficacy in developing prognostic models for stroke mortality. This study enhances stroke mortality prediction and supports clinical decision-making, emphasizing the utility of the support vector machine method.

Keywords: comparative analysis; machine learning; malaysia; prediction models; stroke mortality.