Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement

Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement

Hybrid Gradient Descent Grey Wolf Optimizer; HyperparameterOptimization; DiabetesPrediction; Machine Learning;Support Vector Machine(SVM

Advancements in machine learning have enabled the development of more accurate and efficient health prediction models. This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. SVM is a robust machine learning algorithm for classification and regression. Still, itsperformance depends significantly on selecting appropriate hyperparameters such as regularization (C), kernel coefficient (γ), and polynomial kernel degree (d). The HGD-GWO method synergizes Gradient Descent for local optimization and Gray Wolf Optimizer for global solution exploration. Using the Pima Indians Diabetes dataset, the process includes normalization, hyperparameter optimization, data division, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The optimized SVM achieved an accuracy of 81.17%, with precision, recall, and F1-score values of 75.00%, 57.45%, and 65.06%, respectively, at a data ratio of80%:20%. These findings highlight the potential of HGD-GWO in enhancing predictive models, particularly for early diabetes detection.

Sri Rossa Aisyah Puteri Baharie1*, Sugiyarto Surono2, Aris Thobirin3

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6203/1021

Matematika, Sains dan Teknologi Terapan, Universitas Ahmad Dahlan, Yogyakarta, Indonesia

16-02-2025

FAJAR BAGUS W

PDF

ENGLISH

TEXT