Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer’s Disease With High Performance Computing - Zhang, Fan; Petersen, Melissa; Johnson, Leigh; Hall, James; O’Bryant, Sid E. (Frontiers in Artificial Intelligence, 2021)
Driven by massive datasets comprising 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 in 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 AD ML. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public AD data of MRI along with age, gender and education. Results showed that computational efficiency was increased by 96%, helping 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.