Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis and driving capability prediction

• Published in: Orphanet journal of rare diseases Vol. 18; no. 1; p. 218
• Main Authors: Ouwerkerk, Jasper, Feleus, Stephanie, van der Zwaan, Kasper F, Li, Yunlei, Roos, Marco, van Roon-Mom, Willeke M C, de Bot, Susanne T, Wolstencroft, Katherine J, Mina, Eleni
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27-07-2023; BioMed Central; BMC
• Subjects: Analysis; Care and treatment; Data pre-processing; Datasets; Enroll-HD; Genetic aspects; Huntingtin; Huntington's chorea; Huntington's disease; Huntingtons disease; Learning algorithms; Longitudinal studies; Machine learning; Medical research; Methods; Neural coding; Neural networks; Neurodegenerative diseases; Patients; Polyglutamine; Predictions; Prognosis; Rare diseases; Recurrent neural networks; Trinucleotide repeat diseases; Trinucleotide repeats; Variables; Netherlands; Recurrent neural networks; Huntington’s disease; Data pre-processing; Enroll-HD; Machine learning
• ISSN: 1750-1172; 1750-1172
• DOI: 10.1186/s13023-023-02785-4

In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of different diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirement for large datasets often prevents this approach. Huntington's disease (HD) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the coding region of the huntingtin gene. The world's largest observational study for HD, Enroll-HD, describes over 21,000 participants. As such, Enroll-HD is amenable to ML methods. In this study, we pre-processed and imputed Enroll-HD with ML methods to maximise the inclusion of participants and variables. With this dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well-established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of ML methods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments.
Simple pre-processing imputed around 42% of missing values in Enroll-HD. Also, 167 variables were retained as a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula. The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehn formula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accurate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predicted with an accuracy of 85.2%. The resulting pre-processing workflow and code to train the ML models are available to be used for related HD predictions at: https://github.com/JasperO98/hdml/tree/main .
Our pre-processing workflow made it possible to resolve the missing values and include most participants and variables in Enroll-HD. We show the added value of a ML approach, which improved AAO predictions and allowed for the development of an advisory model that can assist clinicians and participants in estimating future driving capability.