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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Thirteen noninvasive predictors were identified, including \u2018Bathing\u2019, \u2018Age\u2019, \u2018Caregiver\u2019, \u2018Sleep duration\u2019, \u2018Homekeeping\u2019, \u2018Right Ear\u2019, \u2018Stand BWEO\u2019, \u2018Hobbies\u2019, \u2018Focusing Difficulty\u2019, \u2018UI effect\u2019, and \u2018Housework\u2019. 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/25424823261415843","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:49:50Z","timestamp":1770227390000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["An efficient non-invasive model for predicting cognitive impairment based on comprehensive geriatric assessment: Machine learning and SHAP analysis"],"prefix":"10.1177","volume":"10","author":[{"given":"Jia","family":"Zhang","sequence":"first","affiliation":[{"name":"The First Affiliated Hospital of Chongqing Medical University"},{"name":"The First Affiliated Hospital of Chongqing Medical University"},{"name":"Key Laboratory of Digital Health and Intelligent Medicine, Chongqing Municipal Health Commission, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Li","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Chongqing Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sha","family":"Wen","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Chongqing Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangzhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jinan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Gao","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Chongqing Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2290-7469","authenticated-orcid":false,"given":"Yanhui","family":"Feng","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Chongqing Medical University"},{"name":"Key Laboratory of Digital Health and Intelligent Medicine, Chongqing Municipal Health Commission, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9425-2967","authenticated-orcid":false,"given":"Yang","family":"L\u00fc","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Chongqing Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cger.2014.04.001"},{"key":"e_1_3_4_3_2","volume-title":"Global status report on the public health response to dementia","author":"World Health Organization","year":"2021","unstructured":"World Health Organization. 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