The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review
Open accessAbstract
Objective: Machine learning classification has been the most important computational development in the last years to satisfy the primary need of clinicians for automatic early diagnosis and prognosis. Nowadays, Random Forest (RF) algorithm has been successfully applied for reducing high dimensional and multi-source data in many scientific realms. Our aim was to explore the state of the art of the application of RF on single and multi-modal neuroimaging data for the prediction of Alzheimer’s disease. Methods: A systematic review following PRISMA guidelines was conducted on this field of study. In particular, we constructed an advanced query using boolean operators as follows: ("random forest" OR "random forests") AND neuroimaging AND ("alzheimer's disease" OR alzheimer's OR alzheimer) AND (prediction OR classification). The query was then searched in four well-known scientific databases: Pubmed, Scopus, Google Scholar and Web of Science. Results: Twelve articles – published between the 2007 and 2017 - have been included in this systematic review after a quantitative and qualitative selection. The lesson learnt from these works suggest that when RF was applied on multi-modal data for prediction of Alzheimer’s disease (AD) conversion from the Mild Cognitive Impairment (MCI), it produces one of the best accuracies to date. Moreover, the RF has important advantages in terms of robustness to overfitting, ability to handle highly non-linear data, stability in the presence of outliers and opportunity for efficient parallel processing mainly when applied on multi-modality neuroimaging data, such as MRI morphometric, diffusion tensor imaging and PET images. Conclusions: We discussed the strengths of RF, considering also possible limitations and by encouraging further studies on the comparisons of this algorithm with other commonly used classification approaches, particularly in the early prediction of the progression from MCI to AD.
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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gotzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · BMJ · doi:10.1136/bmj.b2700 · Cited by 17,648
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Abraham, A., Milham, M. P., Di Martino, A., Craddock, R. C., Samaras, D., Thirion, B., & Varoquaux, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.045 · Cited by 701
Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.057 · Cited by 622
Fripp, J., Bourgeat, P., Acosta, O., Raniga, P., Modat, M., Pike, K. E., Jones, G., O'Keefe, G., Masters, C. L., Ames, D., et al. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.07.053 · Cited by 80 · cited 1×
“…diffusion tensor imaging (DTI), single photon emission tomography (SPECT), and positron emission tomography (PET) have been widely and successfully applied in the study of MCI and AD (Greicius et al., 2004; Matsuda, 2007; Fripp et al., 2008; Frisoni et al., 2010; Acosta-Cabronero and Nestor, 2014).”
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Cited by
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035
Early prediction of circulatory failure in the intensive care unit using machine learning
Hyland, S. L., Faltys, M., Hüser, M., Lyu, X., Gumbsch, T., Esteban, C., Bock, C., Horn, M., Moor, M., Rieck, B., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0789-4 · Cited by 477
Machine Learning Methods in Drug Discovery
Patel, L., Shukla, T., Huang, X., Ussery, D. W., & Wang, S. (2020) · Molecules · doi:10.3390/molecules25225277 · Cited by 460
Grueso, S., & Viejo-Sobera, R. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00900-w · Cited by 303
An explainable machine learning approach for Alzheimer’s disease classification
Alatrany, A. S., Khan, W., Hussain, A., Kolivand, H., & Al-Jumeily, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-51985-w · Cited by 172
Metadata
- Authors
- Sarica, A., Cerasa, A., & Quattrone, A.
- Venue
- Frontiers in Aging Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2017-10-06
- Volume
- 9
- Pages
- 329-329
- ISSN
- 1663-4365 (ISSN-L)
- OpenAlex
- W2761181345
- Cited by
- 644
- References
- 8
- FWCI
- 3.85
- Type
- journal article
- Language
- en
- Open access
- gold
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- Sarica, A., Cerasa, A., & Quattrone, A. (2017). Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review. Frontiers in Aging Neuroscience. https://doi.org/10.3389/fnagi.2017.00329
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