Logo-hpp
2023: Two-year Impact Factor: 2.4
Scopus Journal Metrics
CiteScore (2023):7.1
 
Platinum
Open Access

Health Promot Perspect. 2025;15(1): 63-72.
doi: 10.34172/hpp.025.43635
  Abstract View: 54
  PDF Download: 46

Original Article

Machine Learning Predictive Models for Survival in Patients with Brain Stroke

Solmaz Norouzi 1 ORCID logo, Samira Ahmadi 2, Shayeste Alinia 3, Farshid Farzipoor 4, Azadeh Shahsavari 5, Ebrahim Hajizadeh 6* ORCID logo, Mohammad Asghari Jafarabadi 7,8,9* ORCID logo

1 Student Research Committee, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
2 Social Determinants of Health Research Center, Health and Metabolic Diseases Research Institute, Zanjan University of Medical Sciences, Zanjan, Iran
3 Department of Statistics and Epidemiology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
4 Department of Health Education and Promotion, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
5 Department of Computer Engineering, Faculty of Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
6 Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
7 Cabrini research, Cabrini health, Melbourne, VIC, 3144, Australia
8 School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, 3004, Australia
9 Department of Psychiatry, School of Clinical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, 3168, Australia
*Corresponding Authors: Ebrahim Hajizadeh, Email: hajizadeh@modares.ac.ir; Mohammad Asghari Jafarabadi, Email: m.asghari862@gmail.com

Abstract

Background: This study aims to harness the predictive power of machine learning (ML) algorithms for accurately predicting mortality and survival outcomes in brain stroke (BS) patients.

Methods: A total of 332 patients diagnosed with BS were enrolled in the study between April 21, 2006, and December 22, 2007, and then followed for 15 years (until 2023). Mortality outcomes were modeled using various statistical techniques, including the Cox model, decision trees, random survival forests (RSF), support vector machines (SVM), gradient boosting, and mboost. The best-performing model was selected based on diagnostic performance metrics: specificity, sensitivity, precision, accuracy, area under the receiver operating characteristic curve (AUC), positive likelihood ratio, negative likelihood ratio, and negative predictive value.

Results: The results indicate that ML models in small sample sizes, particularly the SVM, outperformed the Cox model in predicting mortality and survival over 15 years, achieving an accuracy of 85% and an AUC of 0.765 (95% CI 0.637-0.83). Furthermore, the study identified important variables, including blood pressure history, waterpipe smoking, lack of physical activity, type of cerebrovascular accident, current smoking status, sex, and age, which provide valuable insights for clinicians in risk assessment.

Conclusion: Our study showed that the SVM model outperforms the Cox model in predicting 15-year mortality and survival, particularly in small sample sizes. Moreover, the identification of key risk factors such as blood pressure history, waterpipe smoking, lack of physical activity, type of cerebrovascular accident, current smoking status, sex, and age highlights the need for their consideration in clinical assessments to enhance patient care.


First Name
Last Name
Email Address
Comments
Security code


Abstract View: 55

Your browser does not support the canvas element.


PDF Download: 46

Your browser does not support the canvas element.

Submitted: 04 Sep 2024
Revision: 17 Jan 2025
Accepted: 28 Jan 2025
ePublished: 06 May 2025
EndNote EndNote

(Enw Format - Win & Mac)

BibTeX BibTeX

(Bib Format - Win & Mac)

Bookends Bookends

(Ris Format - Mac only)

EasyBib EasyBib

(Ris Format - Win & Mac)

Medlars Medlars

(Txt Format - Win & Mac)

Mendeley Web Mendeley Web
Mendeley Mendeley

(Ris Format - Win & Mac)

Papers Papers

(Ris Format - Win & Mac)

ProCite ProCite

(Ris Format - Win & Mac)

Reference Manager Reference Manager

(Ris Format - Win only)

Refworks Refworks

(Refworks Format - Win & Mac)

Zotero Zotero

(Ris Format - Firefox Plugin)