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We aimed to develop a single clinically informed model capable of both diagnosing dementia and predicting its onset over flexible time horizons up to 10 years. We trained our ML model on the NACC Uniform Data Set using 20 inputs compatible with UK National Healthcare Service secondary-care workflows, including a\n                    <jats:italic>HORIZON<\/jats:italic>\n                    feature encoding months from baseline. We also integrated explainability and fairness analyses. Same-visit classification achieved sensitivity of 0.951 (95% confidence interval 0.947\u20130.954), specificity of 0.810 (0.805\u20130.814), a geometric mean (G-mean) of 0.877 (0.874\u20130.880) and an area under the receiver operating characteristic curve (AUC) of 0.959 (0.957\u20130.960). Across future horizons from 24 to 120 months, sensitivity\/specificity ranged between 0.808\/0.856 at 24 months and 0.833\/0.706 at 120 months, while AUCs were 0.906 and 0.851. Compared with a Cox proportional hazards model, our flexible-horizon ML model achieved a higher G-mean at four of the five evaluated horizons and higher specificity from 48 months onwards, whereas Cox achieved greater sensitivity from 48 months onwards. Shapley additive explanations highlighted functional independence, prediction horizon and cognitive measures as influential. Exploratory subgroup analyses showed broadly similar balanced discrimination across sex, race and age groups, although sensitivity\u2013specificity trade-offs varied. An interactive web application was also developed for demonstration and clinical feedback. These findings support the feasibility of flexible-horizon, clinically-informed and responsible ML-based decision support for dementia early detection; however, external validation, recalibration and prospective clinical evaluation are required before deployment.\n                  <\/jats:p>","DOI":"10.1007\/s10916-026-02458-2","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T05:41:27Z","timestamp":1789450887000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Flexible-Horizon Clinical Decision Support Model for Dementia Early Detection and Risk Prediction in Secondary Care: A Responsible AI Approach"],"prefix":"10.1007","volume":"50","author":[{"given":"Adnane","family":"Ez-zizi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalyan","family":"Seelam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa","family":"Leggett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bilal R.","family":"Malik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"2458_CR1","unstructured":"National Health Service (NHS). 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No additional ethical approval was required by the authors because this was a secondary analysis of de-identified data from the National Alzheimer\u2019s Coordinating Center (NACC) Uniform Data Set, obtained upon request. The authors did not recruit or interact with participants. NACC obtained written informed consent from all participants and co-participants, and ethical approval was secured before data collection.","order":1,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Clinical Trial Number","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"132"}}