BAYESIAN OPTIMIZATION FOR HYPERPARAMETER TUNING IN HEALTHCARE FOR DIABETES PREDICTION

• Published in: Informing science Vol. 28; p. 1
• Main Authors: Sudhakar, Annapantula, Sujatha, S, Sathiya, M, Sivaramakrishnan, A, Subramanian, Balambigai, Venkata Ramana, K
• Format: Journal Article
• Language: English
• Published: Informing Science Institute 01.01.2025
• Subjects: Algorithms; Analysis; Artificial intelligence; Diabetes therapy; Health care reform; Machine learning; Searches and seizures; India
• ISSN: 1547-9684

Aim/Purpose Traditional hyperparameter tuning methods' inefficiency and low accuracy in diabetes prediction. Application and evaluation of Bayesian optimization aim to increase the accuracy of diabetes prediction models. Background Bayesian optimization (BO) has emerged as a powerful technique for hyperparameter tuning in machine learning models with its methodical approach for optimizing complex functions with expensive evaluations. Selecting the optimal hyperparameters for models can significantly increase prediction accuracy in the healthcare sector, particularly for diabetes prediction, hence improving patient outcomes and resource management. Methodology Among the well-known diabetes databases used in the study is the Pima Indian Diabetes Database. Among the machine learning models developed and whose hyperparameters are modified via Bayesian optimization are Support Vector Machines (SVM), Random Forest, and Gradient Boosting Machines (GBM). The optimization process is compared with traditional tuning methods to assess improvements in model performance. Contribution Problems include the increasing prevalence of diabetes worldwide and the role vital function prediction models play in early diagnosis and therapy. Especially when dealing with enormous datasets common in healthcare, grid search, and random search are sometimes computationally taxing and inefficient. However, Bayesian optimization promises a more practical and economical approach by selecting hyperparameters iteratively based on earlier evaluations. Findings Bayesian optimization not only yields faster calculation times but also outperforms traditional methods. More precisely, models adapted by Bayesian optimization show greater sensitivity and specificity, which are crucial for accurate and prompt diabetes diagnosis. Future Research This work can be improvised using several recent artificial intelligence algorithms with the integration of IoT on real-time datasets. Keywords machine learning, Bayesian, hyperparameter, diabetes prediction, healthcare applications