biostudies-literatureChang CYNIA NIH HHSF. Hoffmann-La Roche26668https://www.ebi.ac.uk/biostudies/studies/S-EPMC11535522biostudies-literatureUnknown14(1)Early detection of Alzheimer's disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The objective was to establish AD progression models through a deep learning approach to analyze heterogeneous, multi-modal datasets, including clustering analyses of population subsets. A multi-head deep-learning architecture was built to process and learn from biomedical and imaging data from the National Alzheimer's Coordinating Center. Shapley additive explanation algorithms for feature importance ranking and pairwise correlation analysis were used to identify predictors of disease progression. Four primary disease progression clusters (slow, modScientific reportsPrediction and clustering of Alzheimer's disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data.PMC11535522P30 AG062677P30 AG066514P30 AG066515P30 AG066518P30 AG062715P30 AG066519P20 AG068053P30 AG072931P30 AG066462P30 AG072975P30 AG072976P20 AG068077P30 AG072977P30 AG066444P30 AG072972P30 AG072973P30 AG066468P30 AG072978R01 AG079280P30 AG072979P30 AG072958P30 AG072959P30 AG062421P30 AG062422P30 AG066546P30 AG066506P30 AG066507P30 AG066508P30 AG062429P30 AG066509P30 AG066530P20 AG068082P30 AG066511P30 AG066512U24 AG072122P20 AG068024P30 AG072946P30 AG072947Win NChang CYSlowiejko DfalsePrediction and clustering of Alzheimer's disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data.Early detection of Alzheimer's disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The objective was to establish AD progression models through a deep learning approach to analyze heterogeneous, multi-modal datasets, including clustering analyses of population subsets. A multi-head deep-learning architecture was built to process and learn from biomedical and imaging data from the National Alzheimer's Coordinating Center. Shapley additive explanation algorithms for feature importance ranking and pairwise correlation analysis were used to identify predictors of disease progression. Four primary disease progression clusters (slow, mod2024-01-01T00:00:00Z2024 Nov2026-07-16T13:17:37.347Z2025-04-20T03:54:13.323ZS-EPMC115355223949671810.1038/s41598-024-77829-1