A nested cross validation approach to machine learning model performance evaluation on a small dataset for Creutzfeldt-Jakob disease diagnosis.

The use of machine learning (ML) to diagnose neurological diseases has become increasingly popular. However, some rare neurodegenerative diseases such as Creutzfeldt-Jakob disease (CJD) suffer from the way that the traditional diagnosis relying abnormal protease-resistant prion protein accumulation in post-mortem brain tissue as the definitive confirmation of the disease such that the possibility of early detection and intervention is very limited. One recent ML study has shown promise in aiding the identification of CJD with greater accuracy and efficiency using protein levels in cerebrospinal fluid that could be obtained in-vivo.