•Robust radiation-induced mucositis models for a clinical cohort of H&N patients.•Two parameter selection methods resulted in very similar models.•Principal component analyses uncoupled the highly correlated DVH metrics.•LASSO & Best Subset found dose and RT acceleration as the most important predictors.•Smoking during RT seems to protect for radiation-induced mucositis. Radiation-induced mucositis is a severe acute side effect, which can jeopardize treatment compliance and cause weight loss during treatment. The study aimed to develop robust models to predict the risk of severe mucositis. Mucosal toxicity scores were prospectively recorded for 802 consecutive Head and Neck (H&N) cancer patients and dichotomised into non-severe event (grade 0–2) and severe event (grade 3+) groups. Two different model approaches were utilised to evaluate the robustness of the models. These used LASSO and Best Subset selection combined with 10-fold cross-validation performed on two-thirds of the patient cohort using principal component analysis of DVHs. The remaining one-third of the patients were used for validation. Model performance was tested through calibration plot and model performance metrics. The main predicted risk factors were treatment acceleration and the first two principal dose components, which reflect the mean dose and the balance between high and low doses to the oral cavity. For the LASSO model, gender and current smoker status were also included in the model. The AUC values of the two models on the validation cohort were 0.797 (95%CI: 0.741–0.857) and 0.808 (95%CI: 0.749–0.859), respectively. The two models predicted very similar risk values with an internal Pearson coefficient of 0.954, indicating their robustness. Robust prediction models of the risk of severe mucositis have been developed based on information from the entire dose distribution for a large cohort of patients consisting of all patients treated H&N for within our institution over a five year period.