Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer's Disease Data

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dc.creatorZhang, Fan
dc.creatorPetersen, Melissa E.
dc.creatorJohnson, Leigh A.
dc.creatorHall, James R.
dc.creatorO'Bryant, Sid E.
dc.creator.orcid0000-0002-3920-5877 (Petersen, Melissa E.)
dc.creator.orcid0000-0001-7769-8417 (Johnson, Leigh A.)
dc.creator.orcid0000-0003-0582-5266 (O'Bryant, Sid)
dc.date.accessioned2023-02-16T14:25:41Z
dc.date.available2023-02-16T14:25:41Z
dc.date.issued2022-11-17
dc.description.abstractAccurate detection is still a challenge in machine learning (ML) for Alzheimer's disease (AD). Class imbalance in imbalanced AD data is another big challenge for machine-learning algorithms working under the assumption that the data are evenly distributed within classes. Here, we present a hyperparameter tuning workflow with high-performance computing (HPC) for imbalanced data related to prevalent mild cognitive impairment (MCI) and AD in the Health and Aging Brain Study-Health Disparities (HABS-HD) project. We applied a single-node multicore parallel mode to hyperparameter tuning of gamma, cost, and class weight using a support vector machine (SVM) model with 10 times repeated fivefold cross-validation. We executed the hyperparameter tuning workflow with R's bigmemory, foreach, and doParallel packages on Texas Advanced Computing Center (TACC)'s Lonestar6 system. The computational time was dramatically reduced by up to 98.2% for the high-performance SVM hyperparameter tuning model, and the performance of cross-validation was also improved (the positive predictive value and the negative predictive value at base rate 12% were, respectively, 16.42% and 92.72%). Our results show that a single-node multicore parallel structure and high-performance SVM hyperparameter tuning model can deliver efficient and fast computation and achieve outstanding agility, simplicity, and productivity for imbalanced data in AD applications.
dc.description.sponsorshipThis research was funded by the National Institute on Aging of the National Institutes of Health under Award Numbers R01AG058537, R01AG054073, R01AG058533, and 3R01AG058533-02S1.
dc.identifier.citationZhang, F., Petersen, M., Johnson, L., Hall, J., & O'Bryant, S. E. (2022). Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer's Disease Data. Applied sciences (Basel, Switzerland), 12(13), 6670. https://doi.org/10.3390/app12136670
dc.identifier.issn2076-3417
dc.identifier.issue13
dc.identifier.urihttps://hdl.handle.net/20.500.12503/32029
dc.identifier.volume12
dc.publisherMDPI
dc.relation.urihttps://doi.org/10.3390/app12136670
dc.rights.holder© 2022 by the authors.
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceApplied Sciences
dc.subjectAlzheimer's disease
dc.subjecthigh-performance computing
dc.subjecthyperparameter tuning
dc.subjectimbalanced data
dc.subjectmachine learning
dc.subjectmild cognitive impairment
dc.titleHyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer's Disease Data
dc.typeArticle
dc.type.materialtext

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