Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients.

Abstract

Background: Depression is a common complication after a stroke that may lead to increased disability and decreased quality of life. The objective of this study was to develop and validate an interpretable predictive model to assess the risk of depression in stroke patients using machine learning (ML) methods.

Methods: This study included 1143 stroke patients from the NHANES database between 2005 and 2020. First, risk factors for depression in stroke patients were determined by univariate and multivariate logistic regression analysis. Next, five machine learning algorithms were used to construct predictive models, and several evaluation metrics (including area under the curve (AUC)) were used to compare the predictive performance of the models. In addition, the SHAP (Shapley Additive Explanations) method was used to rank the importance of features and to interpret the final model.

Results: We screened seven features to construct a predictive model. Among the 5 machine learning models, the XGBoost (extreme gradient boosting) model showed the best discriminative ability, with an AUC of the ROC (receiver operating characteristic curve) in the test set of 0.746 and an accuracy of 0.834. In addition, the prediction results of the XGBoost model were interpreted in detail using the SHAP algorithm. We also developed a web-based calculator that provides a convenient tool for predicting the risk of depression in stroke patients at the following link: https://prediction-model-for-depression.streamlit.app .

Conclusions: Our interpretable machine learning model serves as an auxiliary tool for clinical judgment, aimed at early and effective identification of depression risk in stroke patients.