Prediction and clustering of Alzheimer’s disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data
Year: 2024
Type: article
Abstract: 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, moderate and rapid converters or non-converters) were subdivided into groups by race and sex, yielding 16 sub-clusters of participants with distinct progression patterns. A multi-head and early-fusion convolutional neural network achieved the most competitive performance and demonstrated superiority over a single-head deep learning architecture and conventional tree-based machine-learning methods, with 97% test accuracy, 96% F1 score and 0.19 root mean square error. From 447 features, 2 sets of 100 predictors of disease progression were extracted. Feature importance ranking, correlation analysis and descriptive statistics further enriched cluster analysis and validation of the heterogeneity of risk factors.
Source: Scientific Reports
Authors: Chun Yin Chang, Diana Slowiejko, Nikki Win
Institution: Genentech
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,590
Awards: G1024368718, G1048051091, G1069732457, G117713310, G120189231, G1269895679, G1458362881, G1593128567, G1753564228, G1827328465, G1859882300, G2190936067, G2234397790, G2434699210, G253265979, G2622865600, G2675915175, G2742075923, G343469054, G348743638, G3703765782, G3952091757, G415047313, G4186766937, Nevada Exploratory Alzheimer's Disease Research Center (NVEADRC), UAB Alzheimer's Disease Research Center, G504619356, G5640525220, New Mexico Alzheimer's Disease Research Center, G816726602, G9000000175, G9000000449, G9000004438, G9000011165, G9000011571, G9000015010, G9000015941, G9000038514, G9000041566, G9000044512, G9000056371, G9000059605, G9000060917, G9000063279, G9000065392, G9000065952, G9000079798, G9000081013, G9000082286, G9000085578, G9000087139, G9000087364, G9000091371, G9000100769, G9000112075, G9000114108, G9000115163, G9000134521, G9000148901, G9000182597, G9000183948, G9000208313, G9000224607, G9000226412, G9000254440, G9000266300, G9000271511, G9000290608, G9000305967, G9000315395, G9000320238, G9000330862, G9000331231, G9000332211, G9000341970, UCSD Shiley-Marcos Alzheimer's Disease Research Center P30, Joint Estimate Diffusion Imaging (JEDI) for improved Tissue Characterization and Neural Connectivity in Aging and Alzheimer's Disease, University of Kansas Alzheimer's Disease Research Center (KU ADRC), Alzheimer's Disease Research Center, Johns Hopkins Alzheimer's Disease Research Center, Stanford Alzheimer's Disease Research Center, University of Washington Alzheimer's Disease Research Center, Goizueta Alzheimer's Disease Research Center
OpenAlex ID: W4404039306