Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm

作者：Zhenwen Sheng, Jinke Kuang, Li Yang, Guiyun Wang, Cuihong Gu, Yanxia Qi, Ruowei Wang, Yuehua Han, Jiao Jiao Li, Xia Wang · 发表于：BMC Public Health · 年份：2024 · DOI：10.1186/s12889-024-18855-6 · 被引用次数：11 · 研究领域：Acute Ischemic Stroke Management、Stroke Rehabilitation and Recovery、Dementia and Cognitive Impairment Research

OBJECTIVE: To explore the factors affecting delayed medical decision-making in older patients with acute ischemic stroke (AIS) using logistic regression analysis and the Light Gradient Boosting Machine (LightGBM) algorithm, and compare the two predictive models. METHODS: A cross-sectional study was conducted among 309 older patients aged ≥ 60 who underwent AIS. Demographic characteristics, stroke onset characteristics, previous stroke knowledge level, health literacy, and social network were recorded. These data were separately inputted into logistic regression analysis and the LightGBM algorithm to build the predictive models for delay in medical decision-making among older patients with AIS. Five parameters of Accuracy, Recall, F1 Score, AUC and Precision were compared between the two models. RESULTS: The medical decision-making delay rate in older patients with AIS was 74.76%. The factors affecting medical decision-making delay, identified through logistic regression and LightGBM algorithm, were as follows: stroke severity, stroke recognition, previous stroke knowledge, health literacy, social network (common factors), mode of onset (logistic regression model only), and reaction from others (LightGBM algorithm only). The LightGBM model demonstrated the more superior performance, achieving the higher AUC of 0.909. CONCLUSIONS: This study used advanced LightGBM algorithm to enable early identification of delay in medical decision-making groups in the older patients with AIS....