Research on User Behavior Analysis and Churn Warning Model Construction of Smart Medical Care Integration Platform Based on Machine Learning

目的：探讨智慧医养融合平台的用户行为特征，构建基于机器学习的用户流失预警模型，为平台精细化运营提供决策支持。方法：以安徽省阜阳市某智慧医养融合平台2020年1月至2024年6月的用户数据为研究对象(n = 1237)，采用RFM模型分析用户行为特征，运用K-means算法进行用户分群，构建包括逻辑回归、随机森林、LSTM等多种机器学习模型进行流失预警。结果：用户以60~79岁人群为主(79.55%)，家政服务是最常用服务类型(50.62%)。识别出四类典型用户群体，其中低频–低价值用户的流失风险最高(风险指数为0.68)。LSTM模型展现出最优的预警性能(AUC = 0.961，准确率 = 0.923)，能够提前平均21天预警潜在流失用户。最近一次使用服务的时间间隔(SHAP值 = 0.284)是最重要的预测特征。基于模型预测结果的干预措施使高风险用户留存率提升32.5%。结论：基于机器学习的流失预警模型能有效识别高风险用户，结合用户行为分析的差异化干预策略可显著提升用户留存率，为智慧医养平台的运营优化提供了新思路。Objective: This paper aims to explore user behavior characteristics of smart medical care integration platform and construct a machine learning-based user churn warning model to provide decision support for refined platform operation. Methods: Using user data (n = 1237) from a smart medical care integration platform in Fuyang City, Anhui Province from January 2020 to June 2024 as research subjects, the RFM model was applied to analyze user behavior characteristics. K-means algorithm was used for user clustering, and multiple machine learning models including logistic regression, random forest, and LSTM were constructed for churn warning. Results: Users were predominantly in the 60-79 age group (79.55%), with housekeeping services being the most frequently used service type (50.62%). Four typical user groups were identified, among which low-frequency-low-value users showed the highest churn risk (risk index was 0.68). The LSTM model demonstrated optimal warning performance (AUC = 0.961, accuracy = 0.923) and could predict potential churning users 21 days in advance on average. The time interval since last service use (SHAP value = 0.284) was the most important predictive feature. Intervention measures based on model predictions improved high-risk user retention rate by 32.5%. Conclusion: The machine learning-based churn warning model can effectively identify high-risk users. Differentiated intervention strategies combined with user behavior analysis can significantly improve user retention rates, providing new insights for the operational optimization of smart medical care platforms.