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Chunk #18 — 3. Results — 3.3. Top Significant Features Contributed to the Classification

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Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features.
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Out of the 72 input variables of the Random Forest model (see Materials and Methods section of the Supplementary Materials for details), 29 significant features that contributed to classifying the Memory group from those from the Control group were identified: 21 default mode network connections, 4 alcohol-related items, 3 personality and life experience factors, and 1 PRS (Table 5).