{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:32:26Z","timestamp":1787027546679,"version":"build-2736575974"},"reference-count":25,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Institute on Aging of the National Institutes of Health","award":["R01AG058537"],"award-info":[{"award-number":["R01AG058537"]}]},{"name":"the National Institute on Aging of the National Institutes of Health","award":["R01AG054073"],"award-info":[{"award-number":["R01AG054073"]}]},{"name":"the National Institute on Aging of the National Institutes of Health","award":["R01AG058533"],"award-info":[{"award-number":["R01AG058533"]}]},{"name":"the National Institute on Aging of the National Institutes of Health","award":["3R01AG058533-02S1"],"award-info":[{"award-number":["3R01AG058533-02S1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Accurate detection is still a challenge in machine learning (ML) for Alzheimer\u2019s 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\u2019s bigmemory, foreach, and doParallel packages on Texas Advanced Computing Center (TACC)\u2019s 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.<\/jats:p>","DOI":"10.3390\/app12136670","type":"journal-article","created":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T11:12:35Z","timestamp":1656760355000},"page":"6670","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer\u2019s Disease Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3502-1808","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX 76107, USA"},{"name":"Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX 76107, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melissa","family":"Petersen","sequence":"additional","affiliation":[{"name":"Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX 76107, USA"},{"name":"Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX 76107, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leigh","family":"Johnson","sequence":"additional","affiliation":[{"name":"Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX 76107, USA"},{"name":"Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX 76107, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Hall","sequence":"additional","affiliation":[{"name":"Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX 76107, USA"},{"name":"Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX 76107, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sid E.","family":"O\u2019Bryant","sequence":"additional","affiliation":[{"name":"Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX 76107, USA"},{"name":"Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX 76107, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"798962","DOI":"10.3389\/frai.2021.798962","article-title":"Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer\u2019s Disease With High Performance Computing","volume":"4","author":"Zhang","year":"2021","journal-title":"Front Artif Intell"},{"key":"ref_2","unstructured":"Alzheimer\u2019s Association (2022, March 21). Alzheimer\u2019s Disease Facts and Figures. Available online: https:\/\/www.alz.org\/alzheimers-dementia\/facts-figures."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1093\/geronb\/gbx169","article-title":"Dementia Prevalence in the United States in 2000 and 2012: Estimates Based on a Nationally Representative Study","volume":"73","author":"Hudomiet","year":"2018","journal-title":"J. Gerontol. B Psychol. Sci. Soc. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Al-Jumeily, D., Hussain, A., Mallucci, C., and Oliver, C. (2016). Chapter 1\u2014Early Diagnosis of Neurodegenerative Diseases from Gait Discrimination to Neural Synchronization. Applied Computing in Medicine and Health, Morgan Kaufmann.","DOI":"10.1016\/B978-0-12-803468-2.00001-1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1159\/000518102","article-title":"Using Fractional Anisotropy Imaging to Detect Mild Cognitive Impairment and Alzheimer\u2019s Disease among Mexican Americans and Non-Hispanic Whites: A HABLE Study","volume":"50","author":"Hall","year":"2021","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_6","first-page":"22","article-title":"Levels of alpha-2 Macroglobulin in cognitively normal Mexican- Americans with Subjective Cognitive Decline: A HABLE Study","volume":"10","author":"Hall","year":"2019","journal-title":"Curr. Neurobiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1017\/S1041610217001016","article-title":"Depression, inflammation, and memory loss among Mexican Americans: Analysis of the HABLE cohort","volume":"29","author":"Johnson","year":"2017","journal-title":"Int. Psychogeriatr."},{"key":"ref_8","first-page":"e12263","article-title":"Vascular risk profile and white matter hyperintensity volume among Mexican Americans and non-Hispanic Whites: The HABLE study","volume":"14","author":"King","year":"2022","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.3233\/JAD-210543","article-title":"Proteomic Profiles of Neurodegeneration Among Mexican Americans and Non-Hispanic Whites in the HABS-HD Study","volume":"86","author":"Zhang","year":"2022","journal-title":"J Alzheimer\u2019s Dis."},{"key":"ref_10","first-page":"e12202","article-title":"The Health & Aging Brain among Latino Elders (HABLE) study methods and participant characteristics","volume":"13","author":"Johnson","year":"2021","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_11","first-page":"e12267","article-title":"Neurodegeneration from the AT(N) framework is different among Mexican Americans compared to non-Hispanic Whites: A Health & Aging Brain among Latino Elders (HABLE) Study","volume":"14","author":"Zhang","year":"2022","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e15605","DOI":"10.1097\/MD.0000000000015605","article-title":"The relationship of CRP and cognition in cognitively normal older Mexican Americans: A cross-sectional study of the HABLE cohort","volume":"98","author":"Vintimilla","year":"2019","journal-title":"Medicine"},{"key":"ref_13","first-page":"17","article-title":"Cardiovascular risk factors in Mexico and the United States: A comparative cross-sectional study between the HABLE and MHAS participants","volume":"156","author":"Vintimilla","year":"2020","journal-title":"Gac. Med. Mex."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.3233\/JAD-215620","article-title":"Metabolic Factors Are Related to Brain Amyloid Among Mexican Americans: A HABS-HD Study","volume":"86","author":"Petersen","year":"2022","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kong, J., Kowalczyk, W., Nguyen, D.A., B\u00e4ck, T., and Menzel, S. (2019, January 6\u20139). Hyperparameter Optimisation for Improving Classification under Class Imbalance. Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China.","DOI":"10.1109\/SSCI44817.2019.9002679"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guido, R., Groccia, M.C., and Conforti, D. (2022). A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers. Soft Comput.","DOI":"10.1007\/s00500-022-06768-8"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hancock, J., and Khoshgoftaar, T.M. (2021, January 10\u201312). Impact of Hyperparameter Tuning in Classifying Highly Imbalanced Big Data. Proceedings of the 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI), Las Vegas, NV, USA.","DOI":"10.1109\/IRI51335.2021.00054"},{"key":"ref_18","first-page":"16","article-title":"High-Performance Machine Learning for Large-Scale Data Classification considering Class Imbalance","volume":"2020","author":"Liu","year":"2020","journal-title":"J. Sci. Program."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, J., Nomura, A., Barton, R., Zhang, H., and Matsuoka, S. (2018). Machine Learning Predictions for Underestimation of Job Runtime on HPC System, Springer.","DOI":"10.1007\/978-3-319-69953-0_11"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.3233\/JAD-201254","article-title":"Recursive Support Vector Machine Biomarker Selection for Alzheimer\u2019s Disease","volume":"79","author":"Zhang","year":"2021","journal-title":"J Alzheimer\u2019s Dis"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1002\/alz.12404","article-title":"Characterizing plasma NfL in a community-dwelling multi-ethnic cohort: Results from the HABLE study","volume":"18","author":"Petersen","year":"2022","journal-title":"Alzheimers Dement"},{"key":"ref_22","unstructured":"Hsu, C.-W., Chang, C.-C., and Lin, C.-J. (2003). A Practical Guide to Support Vector Classification, National Taiwan University."},{"key":"ref_23","first-page":"5938","article-title":"mlr: Machine learning in R","volume":"17","author":"Bischl","year":"2016","journal-title":"JMLR"},{"key":"ref_24","unstructured":"Alzheimer\u2019s Association (2022, June 16). Mild Cognitive Impairment (MCI). Available online: https:\/\/www.alz.org\/alzheimers-dementia\/what-is-dementia\/related_conditions\/mild-cognitive-impairment."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v055.i14","article-title":"Scalable Strategies for Computing with Massive Data","volume":"55","author":"Kane","year":"2013","journal-title":"J. Stat. Softw."}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/12\/13\/6670\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:41:37Z","timestamp":1760139697000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/12\/13\/6670"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,1]]},"references-count":25,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["app12136670"],"URL":"https:\/\/doi.org\/10.3390\/app12136670","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,1]]}}}