Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting
Open accessAbstract
Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.
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The multimodal framework proposed by Tong et al. (2017), employing RUSBoost to address class imbalance, achieved overall accuracy 75.2% and balanced accuracy 69.3% for five-class dementia classification.
“the multimodal framework proposed by Tong et al. (2017), which employed the RUSBoost algorithm to address class imbalance, achieved an overall accuracy of 75.2% for the challenging task of five-class dementia classification. However, its balanced accuracy of 69.3%”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Tong et al., 2017
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- Authors
- Tong, T., Ledig, C., Guerrero, R., Schuh, A., Koikkalainen, J., Tolonen, A., Rhodius, H., Barkhof, F., Tijms, B., Lemstra, A. W., Soininen, H., Remes, A. M., Waldemar, G., Hasselbalch, S., Mecocci, P., Baroni, M., Lötjönen, J., Flier, W. V. D., & Rueckert, D.
- Affiliations
- Harvard University (US)
- Imperial College London (GB)
- VTT Technical Research Centre of Finland (FI)
- Amsterdam Neuroscience (NL)
- University of Eastern Finland (FI)
- Copenhagen University Hospital (DK)
- University of Perugia (IT)
- Corresponding author
- Tong, Tong
- ORCID
- Venue
- NeuroImage: Clinical
- Publisher
- Elsevier BV
- Published
- 2017
- Volume
- 15
- Pages
- 613-624
- ISSN
- 2213-1582 (ISSN-L)
- PMC
- PMC5479966
- OpenAlex
- W2626479040
- Cited by
- 54
- References
- 68
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 6 · 2023: 8 · 2022: 5 · 2021: 11 · 2020: 6 · 2019: 8 · 2018: 2 · 2017: 2
- FWCI
- 4.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc-nd)
- UEF eRepo (University of Eastern Finland) (acceptedVersion)
- UEF eRepo (University of Eastern Finland) (submittedVersion, cc-by-nc-nd)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Seventh Framework Programme (611005-PredictND)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- neurodegenerative diseases, differential diagnosis, Alzheimer's disease, frontotemporal lobar degeneration, dementia with Lewy bodies, vascular dementia, subjective memory complaints, class imbalance, RUSBoost, sparsity, cerebrospinal fluid biomarkers, clinical decision support
- MeSH terms
- Aged, Aged, 80 and over, Algorithms, Diagnosis, Differential, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Neurodegenerative Diseases, Support Vector Machine
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