Academic Journal
Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer’s Disease Data
| Title: | Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer’s Disease Data |
|---|---|
| Authors: | Fan Zhang, Melissa Petersen, Leigh Johnson, James Hall, Sid E. O’Bryant |
| Source: | Applied Sciences, Vol 12, Iss 13, p 6670 (2022) |
| Publisher Information: | MDPI AG, 2022. |
| Publication Year: | 2022 |
| Collection: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| Subject Terms: | hyperparameter tuning, high-performance computing, machine learning, imbalanced data, mild cognitive impairment, Alzheimer’s disease, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| More Details: | Accurate 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 12136670 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/12/13/6670; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app12136670 |
| Access URL: | https://doaj.org/article/0bd2a92ff341451e80de217ed653e1d4 |
| Accession Number: | edsdoj.0bd2a92ff341451e80de217ed653e1d4 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 12136670 20763417 |
|---|---|
| DOI: | 10.3390/app12136670 |
| Published in: | Applied Sciences |
| Language: | English |