An Interpretable Depression Prediction Model for the Elderly Based on ISSA Optimized LightGBM
Journal of Beijing Institute of Technology(2023)
摘要
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.
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关键词
the elderly,depression prediction,improved sparrow search algorithm(ISSA),light gra-dient boosting machine (LightGBM),Shapley Additive exPlainations (SHAP)
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