TY - JOUR
T1 - Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer’s Disease With High Performance Computing
AU - Zhang, Fan
AU - Petersen, Melissa
AU - Johnson, Leigh
AU - Hall, James
AU - O’Bryant, Sid E.
N1 - Funding Information:
Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R01AG058537, R01AG054073, R01AG058533, and 3R01AG058533-02S1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
Copyright © 2021 Zhang, Petersen, Johnson, Hall and O’Bryant.
PY - 2021/12/8
Y1 - 2021/12/8
N2 - 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.
AB - 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.
KW - alzheimer’s disease
KW - high performance computing
KW - hyperparameter tuning
KW - machine learning
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85121603689&partnerID=8YFLogxK
U2 - 10.3389/frai.2021.798962
DO - 10.3389/frai.2021.798962
M3 - Article
AN - SCOPUS:85121603689
SN - 2624-8212
VL - 4
JO - Frontiers in Artificial Intelligence
JF - Frontiers in Artificial Intelligence
M1 - 798962
ER -