An Interpretable Depression Prediction Model for the Elderly Based on ISSA Optimized LightGBM

Depression is one of the most severe mental health illnesses among senior citizens. Aiming at the low accuracy and poor interpretability of traditional prediction models, a novel interpretable depression predictive model for the elderly based on the improved sparrow search algorithm (ISSA) optimized light gradient boosting machine (LightGBM) and Shapley Additive exPlainations (SHAP) is proposed. First of all, to achieve better optimization ability and convergence speed, various strategies are used to improve SSA, including initialization population by Halton sequence, generating elite population by reverse learning and multi-sample learning strategy with linear control of step size. Then, the ISSA is applied to optimize the hyper-parameters of light gradient boosting machine (LightGBM) to improve the prediction accuracy when facing massive high-dimensional data. Finally, SHAP is used to provide global and local interpretation of the prediction model. The effectiveness of the proposed method is validated by a series of comparative experiments based on a real-world dataset.