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We examined whether German claims data are suitable for dementia risk prediction, how machine learning (ML) compares to classical regression, and what the important predictors for dementia risk are.

We analyzed data from the largest German health insurance company, including 117,895 dementiafree people age 65+. Followup was 10 years. Predictors were: 23 agerelated diseases, 212 medical prescriptions, 87 surgery codes, as well as age and sex. Statistical methods included logistic regression (LR), gradient boosting (GBM), and random forests (RFs).

Discriminatory power was moderate for LR (Cstatistic = 0.714; 95% confidence interval [CI] = 0.708–0.720) and GBM (Cstatistic = 0.707; 95% CI  = 0.700–0.713) and lower for RF (Cstatistic = 0.636; 95% CI  = 0.628–0.643). GBM had the best model calibration. We identified antipsychotic medications and …