Predicting the Rapid Progression of Mild Cognitive Impairment by Intestinal Flora and Blood Indicators through Machine Learning Method

医学 内科学 丙氨酸转氨酶 胃肠病学 天冬氨酸转氨酶 痴呆 尿酸 生物 生物化学 疾病 碱性磷酸酶
作者
Lingling Wang,Jing Yan,Huiqin Liu,Xiaohui Zhao,Haihan Song,Juan Yang
出处
期刊:Neurodegenerative Diseases [S. Karger AG]
卷期号:23 (3-4): 43-52
标识
DOI:10.1159/000538023
摘要

<b><i>Introduction:</i></b> 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. <b><i>Method:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusion:</i></b> The 15 features (including intestinal flora abundance) could establish an effective model for predicting rapid MCI progression.

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