Machine Learning Predictive Models for Survival in Patients with Brain Stroke
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.
Authors
- Anousha Shahsavari
- Samira Ahmadi (ORCID: https://orcid.org/0000-0002-0590-1129)
- Solmaz Norouzi (ORCID: https://orcid.org/0000-0001-5805-6072)
- Mohammad Asghari Jafarabadi (ORCID: https://orcid.org/0000-0003-3284-9749)
- Shayeste Alinia
- Ebrahim Hajizadeh (ORCID: https://orcid.org/0000-0001-7863-4837)
- Farshid Farzipoor (ORCID: https://orcid.org/0000-0002-1454-2463)
Institutions
- Zanjan University of Medical Sciences (IR)
- Tarbiat Modares University (IR)
- Tabriz University of Medical Sciences (IR)
- Cabrini Hospital (AU)
- Islamic Azad University Shabestar (IR)
- Monash University (AU)
Publication Details
- Journal
- Health Promotion Perspectives
- Published
- 2025-05-06
- DOI
- https://doi.org/10.34172/hpp.025.43635
- Citations
- 1
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Tarbiat Modares University