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Fig. 5 — Integrative machine learning approach to risk prediction for dementia and Alzhei
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Created: 2026-04-21T18:29:40
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ID: paper-fig-paper-40864401-5
Fig. 5Figure 5
Partition of dementia cohort in UKB to subgroups by clinical ICD-10. A Venn diagram of different dementia diagnoses. The AD model consists of patients with F00 or G30 diagnosis (355 + 2216 + 33 + 274 = 2878 positive samples). B Comparison of models’ performance by ROC-AUC mean of 10 different training iterations. Error bars present the standard errors. Bars are colored by their categories: AD, unique vascular dementia, non-vascular dementia, vascular dementia (VaD) and dementia
▸Metadata
| pmid | paper-40864401 |
| caption | Partition of dementia cohort in UKB to subgroups by clinical ICD-10. A Venn diagram of different dementia diagnoses. The AD model consists of patients with F00 or G30 diagnosis (355 + 2216 + 33 + 27 |
| image_url | https://www.ebi.ac.uk/europepmc/articles/PMC12972432/bin/11357_2025_1828_Fig5_HTML.jpg |
| paper_title | Integrative machine learning approach to risk prediction for dementia and Alzheimer's disease. |
| figure_label | Fig. 5 |
| figure_number | 5 |
| _schema_version | 1 |
| source_strategy | pmc_api |
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