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

Che Nawi CMNH, Mohd Hairon S, Wan Yahya WNN, Wan Zaidi WA, Musa KI

Cureus, 2023 Dec;15(12):e50426.
PMID: 38222138 DOI: 10.7759/cureus.50426

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.
Fulltext Machine Learning Application: A Bibliometric Analysis From a Half-Century of Research on Stroke

Che Nawi CMNH, Mohd Hairon S, Wan Yahya WNN, Wan Zaidi WA, Hassan MR, Musa KI

Cureus, 2023 Aug;15(8):e44142.
PMID: 37753006 DOI: 10.7759/cureus.44142

The quick advancement of digital technology through artificial intelligence has made it possible to deploy machine learning to predict stroke outcomes. Our aim is to examine the trend of machine learning applications in stroke-related research over the past 50 years. We used search terms stroke and machine learning to search for English versions of original and review articles and conference proceedings published over the past 50 years in Scopus and Web of Science databases. The Biblioshiny web application was utilized for the analysis. The trend of publication and prominent authors and journals were analyzed and identified. The collaborative network between countries was mapped, and a thematic map was used to monitor the authors' trending keywords. In total, 10,535 publications authored by 44,990 authors from 2,212 sources were retrieved. Two distinct clusters of collaborative network nodes were observed, with the United States serving as a connecting node. Three terms - deep learning, algorithms, and neural networks - are observed in the early stages of the emerging theme. Overall, international research collaborations, the establishment of global research initiatives, the development of computational science, and the availability of big data have facilitated the pervasive use of machine learning techniques in stroke research.
Fulltext Risk Factors for COVID-19 Mortality in Malaysia

Hanis TM, Arifin WN, Musa KI, Rodzlan Hasani WS, Che Nawi CMNH, Shahrani SA, et al.

Malays J Med Sci, 2022 Dec;29(6):123-131.
PMID: 36818910 DOI: 10.21315/mjms2022.29.6.12

BACKGROUND: Understanding the risks of COVID-19 mortality helps in the planning and prevention of the disease. This study aimed to determine the risk factors for COVID-19 mortality in Malaysia.
METHODS: Secondary online data provided by the Ministry of Health, Malaysia and Malaysia's national COVID-19 immunisation programme were used: i) COVID-19 deaths data; ii) vaccination coverage data and iii) population estimate data. Quasi-Poisson regression was performed to determine the risk factors for COVID-19 mortality.
RESULTS: Four risk factors were identified: i) vaccination status (partial versus unvaccinated, incidence rate ratio [IRR]: 0.59; 95% CI: 0.54, 0.64; complete versus unvaccinated, IRR: 0.50; 95% CI: 0.45, 0.56; booster versus unvaccinated, IRR: 0.13; 95% CI: 0.05, 0.26); ii) age group (19 years old-59 years old versus above 60 years old, IRR: 0.90; 95% CI: 0.84, 0.97; 13 years old-18 years old versus above 60 years old, IRR: 0.09; 95% CI: 0.04, 0.19; 6 years old-12 years old versus above 60 years old, IRR: 0.09; 95% CI: 0.03, 0.22; below 5 years old versus above 60 years old, IRR: 0.11; 95% CI: 0.04, 0.23); iii) gender (male versus female, IRR: 1.23; 95% CI: 1.14, 1.32) and iv) comorbidity (yes versus no, IRR: 2.13; 95% CI: 1.96, 2.32).
CONCLUSION: This study highlighted the risk factors for COVID-19 mortality and the benefit of COVID-19 vaccination, especially of booster vaccination, in reducing the risk of COVID-19 mortality in Malaysia.

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