Attribution 4.0 International (CC BY 4.0)2023-02-162023-02-162022-11-17Zhang, 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/app121366702076-3417https://hdl.handle.net/20.500.12503/32029Accurate 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.http://creativecommons.org/licenses/by/4.0/Alzheimer's diseasehigh-performance computinghyperparameter tuningimbalanced datamachine learningmild cognitive impairmentArticle© 2022 by the authors.1213