ArticleApplied sciences (Basel, Switzerland)2022
Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer's Disease Data.
Article in Applied sciences (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Fungal morphotype detection and quantification in microscopic images with TU_MyCo-vision: a user-friendly deep learning object detection tool.Fungal biology and biotechnology · 2026Article
- Hybrid Aquila optimizer-Harris Hawks optimization for CNN hyperparameter tuning in brain tumor classification.Scientific reports · 2026Article
- Optimized scheduling of integrated energy systems considering waste-to-power plants and advanced adiabatic air compression energy storage machines.Scientific reports · 2026Article
- Predictive Model for In-Hospital Death in Older Patients with Type 2 Diabetes Mellitus: A Multicenter Retrospective Study in Southwest China.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach.Proteomics · 2023Article
- Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome.Diagnostics (Basel, Switzerland) · 2023Article
- Comorbidities Incorporated to Improve Prediction for Prevalent Mild Cognitive Impairment and Alzheimer's Disease in the HABS-HD Study.Journal of Alzheimer's disease : JAD · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
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
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