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

Che Muhammad Nur Hidayat Che Nawi1, Suhaily Mohd Hairon1, Wan Nur Nafisah Wan Yahya2

  • 1Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, MYS.

Cureus
|January 15, 2024
PubMed
Summary

Predicting stroke mortality is crucial for patient care. The support vector machine (SVM) model showed the best performance in predicting long-term stroke mortality among four tested methods.

Keywords:
comparative analysismachine learningmalaysiaprediction modelsstroke mortality

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Area of Science:

  • Medical Informatics
  • Biostatistics
  • Public Health

Background:

  • Stroke poses a significant global health challenge, with high mortality and morbidity rates.
  • Accurate long-term outcome prediction is essential for effective clinical management and patient prognosis.
  • Developing reliable models for stroke mortality prediction is a critical research area.

Purpose of the Study:

  • To develop and compare prognostic models for predicting stroke mortality.
  • To evaluate the performance of Cox proportional hazard regression (Cox), support vector machine (SVM), and random survival forest (RSF) models.
  • To identify the most effective model for stroke mortality prediction.

Main Methods:

  • A retrospective cohort study involving 950 acute stroke patients from January 2016 to December 2021.
  • Data included demographics, comorbidities, and interventions, linked with the Malaysian National Mortality Registry for outcome determination.
  • Four survival models were employed: Cox, SVM, random survival forest (RSF), and Cox with Elastic Net (Cox-EN) for feature selection.

Main Results:

  • The support vector machine (SVM) model demonstrated superior performance.
  • SVM achieved high time-dependent AUC values (0.842 at 3 months, 0.846 at 1 year, 0.791 at 3 years) and a C-index of 0.803.
  • All models showed robust calibration, with Brier scores consistently below 0.25.

Conclusions:

  • The support vector machine (SVM) model is highly effective for predicting stroke mortality.
  • This study highlights the utility of SVM in enhancing prognostic accuracy for stroke patients.
  • The findings support improved clinical decision-making and patient management strategies for stroke.