Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement - Article

Aug 4, 2026

Advancements in machine learning enable the development of more accurate, efficient health prediction models. This study improves diabetes prediction performance by optimizing a Support Vector Machine (SVM) model with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. SVM performance depends strongly on hyperparameters including regularization (C), kernel coefficient (γ), and polynomial degree (d). The method combines Gradient Descent for local optimization and Gray Wolf Optimizer for global exploration. Results on the Pima Indians Diabetes dataset show 81.17% accuracy with 75.00% precision, 57.45% recall, and 65.06% F1-score at an 80%:20% split, supporting early diabetes detection potential.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Support Vector Machine and hyperparameters

Optimization challenges and prior approaches

Hybrid HGD-GWO concept

Research Methods

Accredited SINTA 2 Ranking Decree of the Director General of Higher Education, Research, and Technology, No. 158/E/KPT/2021 Validity period from Volume 5 Number 2 of 2021 to Volume 10 Number 1 of 2026 Published online at: [http://jurnal.iaii.or.id](http://jurnal.iaii.or.id) JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol. 9 No. 1 (2025) 146-152 e-ISSN: 2580-0760 Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement Sri Rossa Aisyah Puteri Baharie1*, Sugiyarto Surono2, Aris Thobirin3 1, 2, 3 Matematika, Sains dan Teknologi Terapan, Universitas Ahmad Dahlan, Yogyakarta, Indonesia. [1](1puteri.baharie10@gmail.com)[puteri.baharie10@gmail.com](1puteri.baharie10@gmail.com), [2](2sugiyarto@math.uad.ac.id)[sugiyarto@math.uad.ac.id](2sugiyarto@math.uad.ac.id), [3](3aris.thobi@math.uad.ac.id)[aris.thobi@math.uad.ac.id](3aris.thobi@math.uad.ac.id) Abstract 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, its performance 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 of 80%:20%. These findings highlight the potential of HGD-GWO in enhancing predictive models, particularly for early diabetes detection. Keywords: Hybrid Gradient Descent Grey Wolf Optimizer; Hyperparameter Optimization; Diabetes Prediction; Machine Learning; Support Vector Machine (SVM) How to Cite: S. R. A. Puteri Baharie, Sugiyarto Surono, and Aris Thobirin,“Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement”, J. RESTI (Rekayasa Sist. Teknol. Inf.), vol. 9, no. 1, pp. 146-152, Feb. 2025. DOI: [https://doi.org/10.29207/resti.v9i1.6203](https://doi.org/10.29207/resti.v9i1.6203) 1. Introduction The development of more accurate and efficient prediction models, especially for complex data analysis, has been made possible by advances in science and technology. One area of artificial intelligence known as machine learning allows computers to learn patterns in data without the need for special programs [1] . To maximize the distance between the separating hyperplane and the closest data from each class, Support Vector Machine (SVM) is an ideal marginbased classification method [2] . The advantage of SVMs lies in their ability to use kernel tricks, which allow mapping data to larger dimensions without the need for explicit computation, allowing non-linear data separation with high efficiency. As a result, SVM is often used to solve prediction problems in many fields, such as medical analysis [3] . However, the choice of hyperparameters, such as regulation parameters (C), kernel coefficients (γ), and degree (d) for polynomial kernels, greatly affects the performance of SVM. Hyperparameters determine the generalization and complexity of the model. Failure to optimize it can lead to overfitting or underfitting. Therefore, the main challenge in processing complex data is hyperparameter optimization [4] . Previous studies have used k-Nearest Neighbor (KNN) and Naïve Bayes algorithms to predict diabetes on the Pima Indians Diabetes dataset, and research findi

FAQs

What problem does this study address in diabetes prediction modeling?

The study focuses on improving diabetes prediction performance by strengthening SVM hyperparameter optimization, which strongly affects generalization and model complexity.

How does HGD-GWO work in optimizing the SVM hyperparameters?

HGD-GWO combines Gray Wolf Optimizer for global exploration of a wide parameter space with Gradient Descent for local refinement to reach an optimal solution faster.

What results were achieved using the Pima Indians Diabetes dataset?

On an 80%:20% train-test split, the optimized SVM reached 81.17% accuracy, with precision 75.00%, recall 57.45%, and F1-score 65.06%.

Aug 4, 2026

Upload by Theodore