Table 2.

Improvements in Random Forest classifier performance after incorporating gut microbiome features.

Model	Metric	No microbiome data		Including selected taxa		Including selected taxa – no microbiome data
		Mean	SD	Mean	SD	Difference of means	CI 95% lower	CI 95% upper	P
All biomarkers including Aβ	Accuracy	0.985	0.004	0.999	0.006	0.014	0.013	0.015	5.77 × 10−13
	Specificity	0.948	0.013	0.996	0.019	0.047	0.043	0.052	5.77 × 10−13
All biomarkers excluding Aβ	Specificity	0.413	0.107	0.532	0.074	0.119	0.093	0.145	8.44 × 10−13
Clinical covariates + genetic biomarkers	Accuracy	0.706	0.024	0.755	0.023	0.048	0.042	0.055	5.77 × 10−13
	Sensitivity	0.917	0.036	0.963	0.036	0.046	0.036	0.056	8.38 × 10−13
	Specificity	0.196	0.096	0.249	0.099	0.053	0.026	0.080	0.002
Clinical covariates only	Accuracy	0.674	0.036	0.750	0.019	0.075	0.067	0.083	5.77 × 10−13
	Sensitivity	0.850	0.051	0.967	0.024	0.117	0.105	0.128	5.77 × 10−13

Mean accuracy, sensitivity, and specificity for Random Forest models trained on subsets of AD biomarkers, with or without gut microbiome features (selected MetaPhlAn3 taxa), are presented. Each model was trained on 100 random subsets of the training cohort. Shown are the mean performance metrics of those 100 models on the validation cohort. Models are included if they retained significant ANOVA P values after Bonferroni adjustment across all ANOVAs [groups: no microbiome data, including selected taxa (MetaPhlAn3)]. The corresponding differences of means and 95% confidence intervals (CIs) are reported. P values: Tukey’s post hoc test after ANOVA for each model, additionally adjusted using the Bonferroni method (see Fig. 4 and table S9).