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All participants were classified into normal, mild cognitive impairment (MCI), AD. Neuropsychological scale, epidemiological and laboratory parameters were collected. Four machine learning algorithms, the Least Absolute Shrinkage and Selection Operator (LASSO) regression, Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were employed with 10-fold cross-validation. Model performance was measured using area under the receiver operating characteristics curve (ROC-AUC) and area under precision and recall curves (PR-AUC), classification confusion matrices, sensitivity, accuracy, precision, recall, F1 Score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Models demonstrated high ROC-AUC and satisfactory PR-AUC, with LASSO and SVM excelling in the MCI group (recall: 85.3% and 93.1%; F1 score: 78.4% and 78.3%, respectively). Mini-Mental State Examination (MMSE) scores differed significantly across stages, except for advanced-stage items such as naming, language repetition, and language understanding.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These multi-dimensional machine learning models show promise as effective tools for predicting AD stage stratification, enabling targeted monitoring and early intervention for at-risk patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877251352510","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T09:41:43Z","timestamp":1751017303000},"page":"1593-1614","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Utility of machine learning algorithms in classification of progressive cognitive impairment in Alzheimer's 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