{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T08:14:53Z","timestamp":1775808893771,"version":"3.50.1"},"reference-count":102,"publisher":"Elsevier BV","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SSRN Journal"],"abstract":"<jats:p>&lt;span&gt;Mild cognitive impairment (MCI) and dementia due to Alzheimer\u2019s disease (AD) are neurodegenerative pathologies&amp;nbsp;which cause cognitive, functional, and behavioural changes. While neuroimaging techniques like electroencephalography&amp;nbsp;(EEG) are often used to study disruptions in brain activity, other factors, such as genetics and socio-demographic&amp;nbsp;data, also are useful to obtain a comprehensive characterisation of MCI and AD. These data types are typically assessed&amp;nbsp;independently, missing critical associations that capture the complex pathophysiology of these diseases. To&amp;nbsp;this aim, an integrative Machine Learning-SHapley Additive exPlanations (ML-SHAP) framework was introduced to&amp;nbsp;evaluate feature relevance. The study included 167 participants (43 healthy controls, HC; 45 MCI patients, 43 mild&amp;nbsp;AD individuals, and 36 moderate AD patients). The cascading model achieved accuracies of 92.81% in binary (HC&amp;nbsp;&lt;\/span&gt;&lt;i&gt;vs.&lt;\/i&gt;&lt;span&gt;&amp;nbsp;pathological), 73.65% in 3-class (HC&amp;nbsp;&lt;\/span&gt;&lt;i&gt;vs.&lt;\/i&gt;&lt;span&gt;&amp;nbsp;MCI&amp;nbsp;&lt;\/span&gt;&lt;i&gt;vs.&lt;\/i&gt;&lt;span&gt;&amp;nbsp;AD), and 65.87% in 4-class (HC&amp;nbsp;&lt;\/span&gt;&lt;i&gt;vs.&lt;\/i&gt;&lt;span&gt;&amp;nbsp;MCI&amp;nbsp;&lt;\/span&gt;&lt;i&gt;vs.&lt;\/i&gt;&lt;span&gt;&amp;nbsp;mild AD&amp;nbsp;&lt;\/span&gt;&lt;i&gt;vs.&lt;\/i&gt;&lt;span&gt;&amp;nbsp;moderate&amp;nbsp;AD) classifications. Furthermore, SHAP analysis quantified stage-specific feature importance. EEG features, particularly&amp;nbsp;local activation metrics (in theta and alpha frequency bands) and phase-based global synchronization measures,&amp;nbsp;were found to be relevant across all stages. Genetic factors, including Apolipoprotein E (&lt;\/span&gt;&lt;i&gt;ApoE&lt;\/i&gt;&lt;span&gt;) gene variations, were relevant in differentiating&amp;nbsp;control subjects from MCI and AD in the 3-class classification. Meanwhile, socio-demographic data contributed&amp;nbsp;in distinguishing HCs from pathological groups. This study underscores the importance of combining diverse data&amp;nbsp;sources, demonstrating the potential of integrative frameworks for characterising MCI and AD. Such approaches may&amp;nbsp;enhance diagnostic tools by providing a more comprehensive understanding of these complex conditions.&lt;\/span&gt;<\/jats:p>","DOI":"10.2139\/ssrn.6445818","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T07:22:42Z","timestamp":1775805762000},"source":"Crossref","is-referenced-by-count":0,"title":["&lt;p&gt;An Explainable Cascading Machine Learning Framework for the Multimodal Characterisation of Mild Cognitive Impairment and Dementia Due to Alzheimer\u2019s Disease&lt;\/p&gt;"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4831-6392","authenticated-orcid":true,"given":"V&iacute;ctor","family":"Guti&eacute;rrez-de 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