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Published on: June 9, 2018
Machine learning applications in Huntington's disease prognosis: A review
Lubna M Abu Zohair1, Ruben Andriessen2, Noor Mahmoud3
1School of Mathematical and Computer Sciences, Heriot-Watt University, Dubai, United Arab Emirates.
Insights
Machine learning models significantly improve Huntington's disease (HD) progression prediction over traditional methods. Integrating diverse data enhances accuracy, offering better patient stratification and intervention strategies for HD.
Area of Science:
- Neurology
- Computational Biology
- Biomedical Informatics
Background:
- Huntington's disease (HD) progression prediction is vital for patient care and treatment development.
- Traditional age-CAG models have limitations due to variability from genetic and environmental factors.
- Machine learning (ML) offers advanced methods for integrating complex data to predict HD trajectory.
Purpose of the Study:
- To systematically review and analyze ML approaches for predicting HD onset and progression.
- To compare the efficacy of ML models against traditional age-CAG models.
- To identify key data features and methodological considerations for accurate HD prognostic modeling.
Main Methods:
- Systematic literature review following PRISMA guidelines.
- Searched Web of Science, PubMed, and IEEE Xplore for studies from 2003-2024.
- Assessed methodological quality and risk of bias using the PROBAST tool.
Main Results:
- ML models, especially support vector machines and ensemble methods, outperformed traditional age-CAG models.
- ML accurately predicted premanifest to manifest HD conversion (88-98% accuracy).
- Multimodal data (clinical, imaging, molecular) and longitudinal integration significantly improved prediction of disease decline.
Conclusions:
- Machine learning demonstrates strong potential for enhancing prognostic accuracy in Huntington's disease.
- Multimodal and longitudinal data integration is key for robust HD progression modeling.
- Further large-scale, externally validated studies are needed to address methodological weaknesses and bias.
Abstract:
Understanding the trajectory of Huntington's disease (HD) is critical for patient stratification and the development of targeted interventions. Traditionally, studies relied on age-CAG models to estimate disease onset and progression, based on the well-established relationship between CAG repeat length and age at onset. However, additional genetic, environmental, and clinical factors can cause substantial variability. Recent machine learning approaches integrate clinical, imaging, and molecular data for more precise prediction of disease progression. Following PRISMA guidelines, we systematically reviewed studies on HD onset and progression. Using Web of Science, PubMed, and IEEE Xplore, 20 studies published between 2003 and 2024 met the inclusion criteria. We analyzed the machine learning approaches and input features used, assessed methodological quality, and evaluated risk of bias using the PROBAST tool. Overall, machine learning models, particularly support vector machines and ensemble approaches, consistently outperformed traditional age-CAG models. Several studies predicted conversion from premanifest to manifest HD within 5-10 years with high accuracy (88-98%). Beyond predicting onset, machine learning models have also been used to model dis-ease progression using clinical scores assessing motor, cognitive, and functional impairment. Performance was higher in studies incorporating structural and functional MRI biomarkers, and improved further with longitudinal clinical integration, enabling pre-diction of decline years before symptoms onset. Overall, machine learning shows strong potential to improve prognostic modeling in HD, especially through multimodal and longitudinal data. However, common methodological weaknesses and bias highlight the need for larger, externally validated studies using objective biomarkers.
