Yang Guangmei, Wang Guangdong, Wan Leping, Wang Xinle, He Yan
The Affiliated Encephalopathy Hospital of Zhengzhou University, Zhumadian, Henan, China.
Zhengzhou University, Zhengzhou, Henan, China.
Sci Rep. 2025 Mar 11;15(1):8446. doi: 10.1038/s41598-025-92722-1.
This study aims to construct a prediction model for the demand for medical and daily care services of the elderly and to explore the factors that affect the demand for medical and daily care services of the elderly. In this study, a questionnaire survey on the demand for medical and daily care services of 1291 elderly was conducted using multi-stage stratified whole cluster random sampling. SPSS21.0 statistical analysis software was used to describe the basic data of the elderly statistically, and univariate analysis was used to screen variables for model construction and binary logistic regression analysis. The acquired dataset has class imbalance, and to handle this issue, Synthetic Minority Over Sampling Technique with TomekLink (SMOTE-TomekLink) was adopted to resample the dataset for class-balancing. To improve computational efficiency, we used three algorithms to develop prediction models, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Light Gradient Boosting Machine (LightGBM) algorithms. The performance of each model was measured, and the performance of the prediction model was obtained using the following performance metrics: accuracy (ACC), recall (R), precision (P), F1-score, and area under the receiver operating characteristic (AUC). The prediction models for the medical and daily care services demand of the elderly were developed and validated using 12 and 13 key features, respectively. The LightGBM algorithm emerged as the superior prediction model for estimating the service needs of the elderly. For the medical service demand prediction model, LightGBM achieved an AUC of 0.910 and F1-score of 0.841. In the daily care services demand prediction model, LightGBM demonstrated an AUC of 0.906 and an F1-score of 0.819. In the LightGBM model, the analysis of feature importance indicates that the number of chronic diseases, education level, and financial sources emerge as the most significant predictors for the demand of healthcare services, encompassing both medical and daily care services. Based on questionnaire information combined with feature selection, unbalanced data processing and machine learning methods, this study constructed a machine learning model for predicting the demand for medical and daily care services for the elderly, and analyzed the influencing factors of the demand for medical and daily care services for the elderly, providing a reference for the construction and verification of future prediction models for the demand for medical and daily care services for the elderly.
本研究旨在构建老年人医疗和日常护理服务需求预测模型，并探究影响老年人医疗和日常护理服务需求的因素。本研究采用多阶段分层整群随机抽样方法，对1291名老年人的医疗和日常护理服务需求进行了问卷调查。运用SPSS21.0统计分析软件对老年人的基本数据进行统计描述，并采用单因素分析筛选模型构建变量，进行二元逻辑回归分析。所获取的数据集存在类别不平衡问题，为处理该问题，采用带托梅克链接的合成少数过采样技术（SMOTE-TomekLink）对数据集进行重采样以实现类别平衡。为提高计算效率，我们使用三种算法开发预测模型，包括随机森林（RF）、梯度提升决策树（GBDT）和轻量级梯度提升机（LightGBM）算法。测量了每个模型的性能，并使用以下性能指标获得预测模型的性能：准确率（ACC）、召回率（R）、精确率（P）、F1分数和受试者工作特征曲线下面积（AUC）。分别使用12个和13个关键特征开发并验证了老年人医疗和日常护理服务需求的预测模型。LightGBM算法成为估计老年人服务需求的最优预测模型。对于医疗服务需求预测模型，LightGBM的AUC为0.910，F1分数为0.841。在日常护理服务需求预测模型中，LightGBM的AUC为0.906，F1分数为0.819。在LightGBM模型中，特征重要性分析表明，慢性病数量、教育水平和经济来源是医疗保健服务需求（包括医疗和日常护理服务）最显著的预测因素。基于问卷调查信息，结合特征选择、不平衡数据处理和机器学习方法，本研究构建了老年人医疗和日常护理服务需求预测的机器学习模型，并分析了老年人医疗和日常护理服务需求的影响因素，为未来老年人医疗和日常护理服务需求预测模型的构建和验证提供参考。
