This study aimed to build a predictive model to identify frailty in the French national health data system (SNDS) so as to create a new tool to monitor and anticipate the disability burden associated with population ageing. We developed the model using the 2012 wave of the French Health, Healthcare, and Insurance Survey (ESPS) linked to the SNDS (n = 2,829). This survey used Fried's frailty phenotype as the gold standard. We compared two statistical approaches - logistic regressions (stepwise and LASSO selection) and random forest - to predict frailty probability based on different SNDS healthcare claims. We indirectly validated the model by comparing (1) the predicted frailty prevalence in the overall French population in the SNDS with the expected prevalence and (2) the predictive ability of the model for 6-year mortality with that of Fried's frailty phenotype. Logistic regression with LASSO selection was retained as the best method to predict frailty. After stratification for age, we obtained three models for individuals aged 55-64, 65-74, and ≥ 75 years (AUC = 0.61, 0.76, and 0.80 respectively). Applying these models to the SNDS, frailty prevalence was comparable to expected prevalence in all sex and age groups: overall prevalence = 12.9% (95%CI: 12.9-12.9) in the SNDS versus 12.0% (95%CI: 10.8-13.2) in the ESPS. Predicted frailty probabilities in the SNDS showed a similar strong association with 6-year mortality (HR(frail_probability)=2.6, 95%CI: 1.9-3.5) compared with Fried's phenotype (HR(frail_phenotype)=2.9, 95%CI: 2.1-3.8). Our predictive models are thus useful for estimating frailty probability in the SNDS.Read less <