Preprint · Dec 24, 2025
Development, validation and algorithmic fairness of traditional statistical and machine learning models for dementia risk: a population-based cohort study (Preprint)
By Zheng L, Peng N, Lin T et al.
BACKGROUND Machine learning is increasingly used in dementia risk prediction models, but their clinical utility and algorithmic fairness remain uncertain. OBJECTIVE This study aimed to develop and validate traditional statistical and machine learning models for dementia risk prediction in the general population and to evaluate their fairness across key subgroups. METHODS This population-based cohort study followed 483,824 UK Biobank participants without dementia for up to 13 years. Five models for all-cause dementia were developed using 56 evidence-based predictors: Cox proportional hazards model (Cox-PH), competing event risk model (CR), logistic regression (LR), XGBoost, and LightGBM. An age-only model was also developed for comparison. Model performance was assessed at various risk-stratified thresholds, and algorithmic fairness was evaluated through subgroup analysis. RESULTS During follow-up, 7,087 participants (1.46%) developed dementia. All models showed similar discrimination (C-index: 0.85-0.90). Although traditional statistical models (Cox-PH, CR, and LR) had lower C-indices than XGBoost and LightGBM in the internal validation cohort (C-index: 0.85-0.86 vs. 0.87-0.90), their performance in the external validation cohort was similar (C-index: 0.86-0.87 vs. 0.87). With 20% of the screening population identified as high-risk, detection rates ranged from 74% to 76%, with positive predictive values (PPV) of 5%, while the age-only model achieved 68% detection with PPV of 4% externally. The C index decreased to 0.59-0.70 in those aged over 70 years. CONCLUSIONS Traditional statistical and machine learning models had comparable performance in predicting dementia risk within the general population. However, all models showed limited ability to predict high-risk individuals, especially in older populations, indicating limited clinical utility.