Construction of Depression Prediction Model Based on Machine Learning and Its Interpretability

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Objectives: The aim of this study was to construct depression prediction models based on machine learning algorithms, compared the performance of different machine learning models on depression risk prediction, and interpreted the model. Methods: A total of 2573 participants from the CHARLS database. LASSO and stepwise regression were used to screen for variables. The dataset is randomly divided into training set, validation set and test set according to 6:2:2. SMOTE resampling was used to balance the training set when fitted the model. Nine machine learning algorithms were used to construct the prediction model, inclpuding Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Elastic Network Regression (Enet), Support Vector Machine (SVM), Logistic Regression, Multilayer Perceptron (MLP), and K-Nearest Neighbor (KNN). The prediction ability of each machine learning classifier was evaluated on the test set according to the evaluation index, and the "optimal" model of this study was selected. Subsequently, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to analyze the interpretability of the optimal model. Results: The XGBoost model predicted the best performance among the 9 models. Its AUC value reached 0.908 and the clinical net benefit is the highest. The Delong test showed that there was a significant difference between the ROC curves of XGBoost and the other models (P<0.05). The global interpretation based on SHAP showed that life satisfaction, self-rated health status, sleep duration, and cognitive score were inversely proportional to the SHAP value. Female, rural residents, body aches and pains in any area, non-retirement, and limited Instrumental Activities of Daily Living (IADL) have a positive effect on depression. The local interpretation diagram based on SHAP and LIME showed the personalized risk prediction of a single sample. Conclusions: Machine learning models are an effectively tool for predict the risk of depression. The use of SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations can maximize the clinical advantages of machine learning, which is helpful to predict or detect patients at high risk of depression as early as possible, and to take comprehensive evaluation and early prevention and treatment of depression.

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