Paper Info
Title
Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer's Disease With High Performance Computing
Abstract
Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer's disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.
Year
DOI
Venue
2021
10.3389/frai.2021.798962
FRONTIERS IN ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
machine learning, hyperparameter tuning, alzheimer's disease, high performance computing, support vector machine
Journal
4
ISSN
Citations 
PageRank 
2624-8212
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Fan Zhang109.13
Melissa Petersen200.34
Leigh Johnson300.34
James Hall400.34
Sid E O'Bryant500.34