Predicting User Churn of Smart Home-based Care Services Based on SHAP Interpretation

[Objective] This study constructs a user churn prediction model for smart home-based care services. It utilizes the SHAP interpretation method to analyze the impact of different features on user churn. [Methods] First, we retrieved more than 300, 000 community home-based care service orders from 2019 to 2021. Then, we incorporated the RFM model (RFM-MLP), the Maslow’s hierarchy of demand theory, the Anderson model, and the Boruta algorithm to identify 11 characteristics across three categories: user values, service selections, and individual features. Third, we chose the XGBoost model from the five established machine learning models for the best performance in predicting user churn. Finally, we employed the SHAP interpretation method to examine the feature impact, dependence, and single-sample analysis. [Results] The predictive model achieves high accuracy and F1 score of approximately 87%. Noteworthy features for predicting user churn on smart home-based care services include domestic service purchase numbers, use length, and user age. [Limitations] Our data was from a single region. The data quality and algorithm complexity could be improved in the future. [Conclusions] The SHAP interpretation method effectively balances accuracy and interpretability in machine learning prediction models. The insights gained provide a foundation for optimizing operational strategies and content design on smart home-based care service platforms. © 2024 Chinese Academy of Sciences. All rights reserved.