{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T20:01:13Z","timestamp":1760990473217,"version":"3.40.4"},"reference-count":30,"publisher":"S. Karger AG","issue":"3-4","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neurodegener Dis"],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p>&lt;b&gt;&lt;i&gt;Introduction:&lt;\/i&gt;&lt;\/b&gt; The aim of the work was to establish a prediction model of mild cognitive impairment (MCI) progression based on intestinal flora by machine learning method. &lt;b&gt;&lt;i&gt;Method:&lt;\/i&gt;&lt;\/b&gt; A total of 1,013 patients were recruited, in which 87 patients with MCI finished a two-year follow-up. To establish a prediction model, 61 patients were randomly divided into a training set and 26 patients were divided into a testing set. A total of 121 features including demographic characteristics, hematological indicators, and intestinal flora abundance were analyzed. &lt;b&gt;&lt;i&gt;Results:&lt;\/i&gt;&lt;\/b&gt; Of the 87 patients who finished a two-year follow-up, 44 presented rapid progression. Model 1 was established based on 121 features with the accuracy 85%, sensitivity 85%, and specificity 83%. Model 2 was based on the first fifteen features of model 1 (triglyceride, uric acid, alanine transaminase, F-Clostridiaceae, G-Megamonas, S-Megamonas, G-Shigella, G-Shigella, S-Shigella, average hemoglobin concentration, G-Alistipes, S-Collinsella, median cell count, average hemoglobin volume, low-density lipoprotein), with the accuracy 97%, sensitivity 92%, and specificity 100%. Model 3 was based on the first ten features of model 1, with the accuracy 97%, sensitivity 86%, and specificity 100%. Other models based on the demographic characteristics, hematological indicators, or intestinal flora abundance features presented lower sensitivity and specificity. &lt;b&gt;&lt;i&gt;Conclusion:&lt;\/i&gt;&lt;\/b&gt; The 15 features (including intestinal flora abundance) could establish an effective model for predicting rapid MCI progression. <\/jats:p>","DOI":"10.1159\/000538023","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T22:00:32Z","timestamp":1709157632000},"page":"43-52","source":"Crossref","is-referenced-by-count":2,"title":["Predicting the Rapid Progression of Mild Cognitive Impairment by Intestinal Flora and Blood Indicators through Machine Learning 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