## 1 Introduction  
Over the last few years, machine learning (ML) has become an important research topic in the high-performance computing (HPC) community. HPC provides a large amount of opportunities, in terms of environments and resources, to help accelerate the process of ML. The ML community has also started to utilize the performance of HPC for better parallelization and scalability. For example, the number of articles on ML and HPC has reached 321,000,000 according to the search results from Google. However, applying ML and HPC to Alzheimer's disease (AD) research is still relatively new. There was only 1 hit [1] from a PubMed search of ML, HPC, and AD (and 84 hits from Google), although the number of publications pertaining to ML and AD has greatly increased, from 294 in 2020 to 1582 in 2022. Alzheimer's is the most common cause of dementia, accounting for 60-80% of dementia cases. Its prevalence rate is about 11% among those aged 65 and older [2, 3]. Collecting AD data while staying as close as possible to the prevalence rate in the population may create imbalanced data, which makes ML challenging [4]. For example, the Health and Aging Brain Study-Health Disparities (HABS-HD) data used here contained 1328 normal controls and 377 mild cognitive impairments (MCIs) and ADs [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. In our preliminary analysis,all positive samples in the testing set were classified wrongly as normal, although our training set had almost 100% sensitivity and specificity. This was because the model learned from the imbalanced training set contained biases and made prediction too sensitive to the majority class, which consisted of normal controls for our HABS-HD data. There are three ways to solve the imbalanced dataset problem: (1) downsampling, (2) upsampling, and (3) class weight optimization. Downsampling involves randomly removing observations from the majority class. Upsampling is the process of randomly duplicating observations from the minority class. However, downsampling will almost always lose information, while upsampling may lead to overestimation of the model performance and makes overfitting likely. Hyperparameter tuning with class weight optimization has proven to be efficient in handling imbalanced data [15; 16; 17]. For example, [PERSON] et al. worked with machine learning algorithms and imbalanced big data and found that, regardless of the classifier or encoding technique for categorical features, classifiers with tuned hyperparameters could yield better results than those with default values when classifying highly imbalanced big data [17]. [PERSON] et al. compared hyperparameter optimization to default hyperparameters for both classification algorithms and resampling approaches and found that hyperparameter optimization could produce better results when classifying the imbalanced datasets [15]. [PERSON] et al. presented a hyperparameter tuning method to improve model performance for imbalanced data [16]. HPC provides great opportunities to look into the imbalanced data problem while accelerating hyperparameter tuning efficiently [18; 19]. In our previous work, we found that HPC could be used to significantly reduce computational time while maintaining the necessary accuracy for balanced AD data [1]. However, applying the workflow from [1] to imbalanced big data may cause an out-of-memory problem because the addition of a third parameter causes the memory usage to increase by x times, where x is the length of the class weight (normally, x is between 10 and 100). Loading the big imbalanced data chunk-by-chunk and then tuning it partially will not result in the same best parameters for all the chunks. Therefore, in this paper, we describe a hyperparameter tuning workflow with HPC and memory management for imbalanced data relating to prevalent mild cognitive impairment and Alzheimer's disease in the HABS-HD project.