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. Aim-ing at the low accuracy and poor interpretability of traditional prediction models, a novel inter-pretable depression predictive model for the elderly based on the improved sparrow search algo-rithm (ISSA) optimized light gradient boosting machine (LightGBM) and Shapley Additive exPlainations (SHAP) is proposed. First of all, to achieve better optimization ability and conver-gence speed, various strategies are used to improve SSA, including initialization population by Hal-ton 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 pre-diction model. The effectiveness of the proposed method is validated by a series of comparative experiments based on a real-world dataset.