{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T04:50:22Z","timestamp":1791521422282,"version":"4.3.4"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cognitive recovery varies substantially among patients with post-stroke mild cognitive impairment (PSCI-MCI), whereas individualized models for predicting post-treatment cognitive improvement remain limited. This study aimed to develop and internally validate a machine learning model for predicting cognitive improvement after 1\u202fmonth of scalp cluster needling therapy with prolonged needle retention (SCNT-PR) in patients with PSCI-MCI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included hospitalized patients diagnosed with PSCI-MCI between January 1, 2010, and January 1, 2023. Baseline data included demographics, vascular risk factors, stroke severity, imaging features, cognitive and functional assessments, emotional and sleep-related indicators, treatment-related variables, and laboratory findings. The outcome was defined as an increase of at least 2 points in the corrected Montreal Cognitive Assessment (MoCA) score after treatment compared with baseline. Least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm were used to identify key predictors. Seven supervised machine learning algorithms were applied to develop pretreatment prediction models. Model performance was assessed in terms of discrimination, calibration, and clinical utility, and Shapley Additive Explanations (SHAP) were used for model interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 750 patients were included and randomly assigned to a training set (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f526) and a validation set (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f224). Seven key predictors were identified from the intersection of LASSO regression and Boruta. Among the evaluated models, LightGBM showed the acceptable performance, with an area under the receiver operating characteristic curve of 0.759. Calibration curves and decision curve analysis indicated acceptable calibration and favorable clinical utility. SHAP analysis identified baseline corrected MoCA score, years of education, National Institutes of Health Stroke Scale total score, Barthel Index score for activities of daily living, age, frontal lobe lesion, and diabetes as the major contributors to model prediction. Sensitivity analyses demonstrated consistent predictor patterns under alternative outcome definitions and predictor configurations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed and internally validated a machine learning model based on multidimensional pretreatment clinical data to predict cognitive improvement after SCNT-PR in patients with PSCI-MCI. This model may provide preliminary support for patient stratification and individualized clinical assessment, although external validation in independent cohorts is required before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2026.1884682","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T04:25:57Z","timestamp":1791519957000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A machine learning model for predicting cognitive improvement after scalp cluster needling therapy in patients with post-stroke mild cognitive impairment"],"prefix":"10.3389","volume":"17","author":[{"given":"Yican","family":"Hao","sequence":"first","affiliation":[{"name":"The Second Affiliated Hospital of Henan University of Chinese Medicine","place":["Zhengzhou, China"]},{"name":"The Second School of Clinical Medicine, Henan University of Chinese 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