Friedreich ataxia (FRDA) requires an objective measure of severity to overcome the shortcoming of clinical scales when applied to trials for treatments. This is hindered due to the rarity of the disease resulting in small datasets. Further, the published quantitative measures for ataxia do not incorporate or underutilise expert knowledge. Bayesian Networks (BNs) provide a structure to adopt both subjective and objective measures to give a severity value while addressing these issues. The BN presented in this paper uses a hybrid learning approach, which utilises both subjective clinical assessments as well as instrumented measurements of disordered upper body movement of individuals with FRDA. The final model's estimates gave a 0.93 Pearson correlation with low error, 9.42 root mean square error and 7.17 mean absolute error. Predicting the clinical scales gave 94% accuracy for Upright Stability and Lower Limb Coordination and 67% accuracy for Functional Staging, Upper Limb Coordination and Activities of Daily Living. Clinical relevance- Due to the nature of rare diseases conventional machine learning is difficult. Most clinical trials only generate small datasets. This approach allows the combination of expert knowledge with instrumented measures to develop a clinical decision support system for the prediction of severity.

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