Guo Kun, Zhu Bo, Zha Lei, Shao Yuan, Liu Zhiqin, Gu Naibing, Chen Kongbo
Xi'an Central Hospital, Xi'an, China.
Tongchuan Mining Bureau Central Hospital, Tongchuan, China.
Front Neurol. 2025 Mar 4;16:1522868. doi: 10.3389/fneur.2025.1522868. eCollection 2025.
Ischemic Stroke (IS) stands as a leading cause of mortality and disability globally, with an anticipated increase in IS-related fatalities by 2030. Despite therapeutic advancements, many patients still lack effective interventions, underscoring the need for improved prognostic assessment tools. Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.
The aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.
A retrospective observational cohort study was conducted involving 398 patients diagnosed with Acute Cerebral Infarction from January 2023 to February 2024. The dataset included demographic information, medical histories, clinical evaluations, and laboratory results. Six ML models were constructed: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, XGBoost, and AdaBoost. Model performance was evaluated using the Area Under the Receiver Operating Characteristic curve (AUC), sensitivity, specificity, predictive values, and F1 score, with five-fold cross-validation to ensure robustness.
The training set, identified key variables associated with stroke prognosis, including hypertension, diabetes, and smoking history. The SVM model demonstrated exceptional performance, with an AUC of 0.9453 on the training set and 0.9213 on the validation set. A Nomogram based on Logistic Regression was developed for visualizing prognostic risk, incorporating factors such as the National Institutes of Health Stroke Scale (NIHSS) score, Barthel Index (BI), Watanabe Drinking Test (KWST) score, Platelet Distribution Width (PDW), and others. Our models showed high predictive accuracy and stability across both datasets.
This study presents a robust ML approach for predicting stroke prognosis, with the SVM model and Nomogram providing valuable tools for clinical decision-making. By incorporating advanced ML techniques, we enhance the precision of prognostic assessments and offer a theoretical and practical framework for clinical application.
缺血性中风（IS）是全球死亡和残疾的主要原因之一，预计到2030年与IS相关的死亡人数将会增加。尽管治疗方法有所进步，但许多患者仍然缺乏有效的干预措施，这突出了改进预后评估工具的必要性。机器学习（ML）模型已成为预测中风预后的有前途的工具，在准确性和速度方面超过了传统方法。
本研究旨在利用中国两个医疗中心的临床数据，开发和验证用于预测急性脑梗死患者6个月预后的ML算法，并评估在临床环境中实施可解释ML的可行性。
进行了一项回顾性观察队列研究，纳入了2023年1月至2024年2月期间诊断为急性脑梗死的398例患者。数据集包括人口统计学信息、病史、临床评估和实验室检查结果。构建了六个ML模型：逻辑回归、朴素贝叶斯、支持向量机（SVM）、随机森林、XGBoost和AdaBoost。使用受试者操作特征曲线下面积（AUC）、敏感性、特异性、预测值和F1分数评估模型性能，并进行五折交叉验证以确保稳健性。
训练集确定了与中风预后相关的关键变量，包括高血压、糖尿病和吸烟史。SVM模型表现出色，在训练集上的AUC为0.9453，在验证集上为0.9213。基于逻辑回归开发了列线图以可视化预后风险，纳入了美国国立卫生研究院卒中量表（NIHSS）评分、巴氏指数（BI）、渡边饮水试验（KWST）评分、血小板分布宽度（PDW）等因素。我们的模型在两个数据集上均显示出较高的预测准确性和稳定性。
本研究提出了一种强大的ML方法来预测中风预后，SVM模型和列线图为临床决策提供了有价值的工具。通过纳入先进的ML技术，我们提高了预后评估的精度，并为临床应用提供了理论和实践框架。
