Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer’s Disease Data

Zhang, Fan; Petersen, Melissa; Johnson, Leigh; Hall, James; O’Bryant, Sid

doi:10.3390/app12136670

Cited by 9 publications

(7 citation statements)

References 21 publications

(41 reference statements)

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“…To deal with detection of diverse patterns across biomarkers, we suggest using different normalization techniques. Furthermore, instead of applying additional methods to deal with severe class imbalances, we suggest optimizing the hyperparameters of the ML models through grid search or cross‐validation to find the appropriate models that work effortlessly with the data of interest [22–24].…”

Section: Introductionmentioning

confidence: 99%

“…For a new observation, the class with the largest discriminant value is selected.To deal to severe class imbalances, our approach to data analysis utilizes the methods of hyperparameter tuning. Past studies have indicated that model performance could be enhanced using hyperparameter tunning methods[22][23][24]. The training of decision tree and random forest included hyperparameter tuning using grid search technique.…”

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confidence: 99%

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Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach

et al. 2023

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The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for scientific discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high‐dimensionality, large size, and nonlinearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, we propose a “wisdom of the crowd” approach to simultaneously predict rare cell populations and perform feature selection by integrating a pool of modern machine learning (ML) algorithms. Given that our approach integrates superior performing ML models across different normalization techniques based on entropy and rank, our method can detect diverse patterns existing across the model features. Furthermore, the method identifies a dynamic biomarker structure that divides the features into persistently selected, unselected, and fluctuating assemblies indicating the role of each biomarker in rare cell prediction, which can subsequently aid in studies of disease progression.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach

et al. 2023

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“…Hyperparameter tuning with class weight optimization is an efficient way to handle imbalanced data [98][99][100][101]. Zhang et al [99] proposed an SVM hyperparameter tuning model with high computing performance.…”

Section: Benchmark Imbalanced Datasetsmentioning

confidence: 99%

An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review

Guido,

Ferrisi,

Lofaro

et al. 2024

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Support vector machines (SVMs) are well-known machine learning algorithms for classification and regression applications. In the healthcare domain, they have been used for a variety of tasks including diagnosis, prognosis, and prediction of disease outcomes. This review is an extensive survey on the current state-of-the-art of SVMs developed and applied in the medical field over the years. Many variants of SVM-based approaches have been developed to enhance their generalisation capabilities. We illustrate the most interesting SVM-based models that have been developed and applied in healthcare to improve performance metrics on benchmark datasets, including hybrid classification methods that combine, for instance, optimization algorithms with SVMs. We even report interesting results found in medical applications related to real-world data. Several issues around SVMs, such as selection of hyperparameters and learning from data of questionable quality, are discussed as well. The several variants developed and introduced over the years could be useful in designing new methods to improve performance in critical fields such as healthcare, where accuracy, specificity, and other metrics are crucial. Finally, current research trends and future directions are underlined.

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“…In the following sections of this article, we will discuss the particulars of Stacked Multi-Kernel SVM and Random Forest models, highlighting their unique advantages and reasoning behind choosing them for diabetes identification. We will focus on exploring hyperparameter tuning techniques that illuminate the methodologies used to optimize the efficiency of these models [29]. Through this comprehensive analysis, we aim to provide valuable insights that extend beyond theoretical considerations and have practical applications in the field of diabetes diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Predictive Performance: Hyperparameter Tuning in Stacked Multi-Kernel Support Vector Machine Random Forest Models for Diabetes Identification

Saputra,

Ma'arif,

Sunat

2024

Journal of Robotics and Control

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This study addresses the necessity for more advanced diagnostic tools in managing diabetes, a chronic metabolic disorder that leads to disruptions in glucose, lipid, and protein metabolism caused by insufficient insulin activity. The research investigates the innovative application of machine learning models, specifically Stacked Multi-Kernel Support Vector Machines Random Forest (SMKSVM-RF), to determine their effectiveness in identifying complex patterns in medical data. The innovative ensemble learning method SMKSVM-RF combines the strengths of Support Vector Machines (SVMs) and Random Forests (RFs) to leverage their diversity and complementary features. The SVM component implements multiple kernels to identify unique data patterns, while the RF component consists of an ensemble of decision trees to ensure reliable predictions. Integrating these models into a stacked architecture allows SMKSVM-RF to enhance the overall predictive performance for classification or regression tasks by optimizing their strengths. A significant finding of this study is the introduction of SMKSVM-RF, which displays an impressive 73.37% accuracy rate in the confusion matrix. Additionally, its recall is 71.62%, its precision is 70.13%, and it has a noteworthy F1-Score of 71.34%. This innovative technique shows potential for enhancing current methods and developing into an ideal healthcare system, signifying a noteworthy step forward in diabetes detection. The results emphasize the importance of sophisticated machine learning methods, highlighting how SMKSVM-RF can improve diagnostic precision and aid in the continual advancement of healthcare systems for more effective diabetes management.

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Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer’s Disease Data

Cited by 9 publications

References 21 publications

Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach

Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach

An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review

Optimizing Predictive Performance: Hyperparameter Tuning in Stacked Multi-Kernel Support Vector Machine Random Forest Models for Diabetes Identification

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