An efficient non-invasive model for predicting cognitive impairment based on comprehensive geriatric assessment: Machine learning and SHAP analysis.

Background: With global aging, cognitive impairment, including Alzheimer's disease and related dementias, has become a critical public health challenge, driving the need for convenient screening tools to facilitate early intervention. Objective: This study aimed to develop an efficient and noninvasive risk assessment model for identifying potential cognitive impairment in the elderly using machine learning algorithms based on comprehensive geriatric assessment (CGA). Methods: We included 1410 participants aged 50 and older from geriatric clinics and community. Feature selection was performed on the CGA indicators using a combination of expert knowledge and machine learning. Logistic regression (LR), naive Bayes, support vector machines, neural networks, and random forests were comprehensively evaluated based on common classification performance metrics. The optimal machine learning algorithms and feature subset are used to construct the final prediction model. Shapley Additive exPlanations (SHAP) was used to explain the model. Results: Thirteen noninvasive predictors were identified, including 'Bathing', 'Age', 'Caregiver', 'Sleep duration', 'Homekeeping', 'Right Ear', 'Stand BWEO', 'Hobbies', 'Focusing Difficulty', 'UI effect', and 'Housework'. The LR model performed best on the test set, with an AUC of 0.877 and high accuracy (0.815), sensitivity (0.767), and specificity (0.827). The SHAP results illustrated the role of these key features in cognitive impairment, which is highly consistent with clinical knowledge. Conclusions: This study identifies convenient, noninvasive predictors for screening-oriented prediction of cognitive impairment, develops an efficient machine learning model, and employs SHAP analysis for interpretation. This facilitates widespread screening, providing guidance for early detection and intervention in high-risk populations.