Norouzi Solmaz, Ahmadi Samira, Alinia Shayeste, Farzipoor Farshid, Shahsavari Azadeh, Hajizadeh Ebrahim, Asghari Jafarabadi Mohammad
Student Research Committee, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Social Determinants of Health Research Center, Health and Metabolic Diseases Research Institute, Zanjan University of Medical Sciences, Zanjan, Iran.
Health Promot Perspect. 2025 May 6;15(1):63-72. doi: 10.34172/hpp.025.43635. eCollection 2025 May.
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.
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.
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.
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.
本研究旨在利用机器学习（ML）算法的预测能力，准确预测脑卒中（BS）患者的死亡率和生存结果。
2006年4月21日至2007年12月22日期间，共有332例被诊断为BS的患者纳入本研究，随后进行了15年的随访（直至2023年）。使用各种统计技术对死亡率结果进行建模，包括Cox模型、决策树、随机生存森林（RSF）、支持向量机（SVM）、梯度提升和mboost。根据诊断性能指标选择表现最佳的模型：特异性、敏感性、精度、准确性、受试者工作特征曲线下面积（AUC）、阳性似然比、阴性似然比和阴性预测值。
结果表明，小样本量的ML模型，尤其是SVM，在预测15年的死亡率和生存率方面优于Cox模型，准确率达到85%，AUC为0.765（95%CI 0.637-0.83）。此外，该研究确定了重要变量，包括血压病史、水烟吸食、缺乏体育活动、脑血管意外类型、当前吸烟状况、性别和年龄，这些为临床医生进行风险评估提供了有价值的见解。
我们的研究表明，SVM模型在预测15年死亡率和生存率方面优于Cox模型，尤其是在小样本量的情况下。此外，确定血压病史、水烟吸食、缺乏体育活动、脑血管意外类型、当前吸烟状况、性别和年龄等关键风险因素，凸显了在临床评估中考虑这些因素以加强患者护理的必要性。
