Continuous Optimization of a Hierarchical Bayesian Network for Friedreich's Ataxia Severity Classification.

Machine learning algorithms for rare disorders, such as Friedreich's Ataxia (FRDA), often suffer from a lack of data. Therefore, the ability for continuous optimization of an objective assessment model would be very useful as a clinical decision support system. In this study, we propose a Bayesian Network(BN) system for FRDA severity estimation that incorporates a Bayesian Statistical updating system to continuously improve the predictive ability while providing an easily interpretable graphical model.