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Using random forest models, we identified specific feature combinations that achieved predictive areas under the curve (AUCs) of 0.78 for SCD, 0.84 for MCI, and 0.98 for AD, highlighting EEG as a particularly effective early screening tool.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>This study elucidates AD's pathophysiological progression and highlights the potential of machine learning\u2010assisted multimodal strategies for early detection and staging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Early electroencephalogram (EEG) changes and compensatory metabolomic responses define subjective cognitive decline (SCD) stage.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>In mild cognitive impairment (MCI), gut\u2013brain axis dysfunction alters microbial diversity and functional pathways.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>In Alzheimer's disease (AD), systemic breakdown disruption enables near\u2010perfect machine learning (ML) detection.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Random forest models yield predictive areas under the curve (AUCs) of 0.78 (SCD), 0.84 (MCI), 0.98 (AD).<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>EEG is a convenient, cost\u2010efficient marker for early screening.<\/jats:p>\n                        <\/jats:list-item>\n   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