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

Ouwerkerk, J.; Feleus, S.; Zwaan, K.F. van der; Li, Y.L.; Roos, M.; Roon-Mom, W.M.C. van; Bot, S.T. de; Wolstencroft, K.J.; Mina, E.

doi:doi:10.1186/s13023-023-02785-4

Background In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of dif‑
ferent 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...Show moreBackground In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of dif‑
ferent 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.
Results 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 accu‑
rate 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/ Jaspe rO98/ hdml/ tree/ main.
Conclusions Our pre‑processing workflow made it possible to resolve the missing values and include most par‑
ticipants 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.
Show less

Leiden University Scholarly Publications

View statistics

Documents

In Collections

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