Establishing a Cross-Population Machine Learning Model for Alzheimer’s Disease Risk Prediction in the Taiwanese Population

Alzheimer's disease (AD) is the most common form of dementia worldwide, but most existing risk prediction models are based on European populations and lack generalizability to other ethnic groups. This study combined genetic and clinical data from both the UK Biobank and a Taiwanese population to develop a model more broadly applicable across populations. By selecting SNPs with similar minor allele frequencies (MAF) between groups and performing genotype imputation, models were built using polygenic risk scores (PRS), genotype, and clinical data. Logistic regression, XGBoost, and multilayer perceptron (MLP) were used for comparison. All models performed well on the UKB validation set (AUC = 0.81), and the logistic regression and MLP models showed improved performance (AUC = 0.87) in the Taiwanese test set. Key predictive features included PRSice2_PRS, lassosum_PRS, and age. The results highlight the potential of integrating genetic and clinical data to improve risk prediction for AD across populations, offering insights into AD pathogenesis and aiding the development of precision medicine strategies.