Combining SVM and Gradient Boosting for Enhanced Accuracy in Diabetes Diagnosis
DOI:
https://doi.org/10.69968/ijisem.2025v4i108-16Keywords:
Diabetes detection, machine learning, Support Vector Machine, Gradient Boosting, voting ensemble, early diagnosisAbstract
To improve prediction accuracy, this work introduces a voting ensemble model that combines Support Vector Machine (SVM) and Gradient Boosting to enable early identification of diabetes. For the first round of testing, we used 89.4–98.0% accuracy with more conventional machine learning models like Decision Tree, Random Forest, and K-Nearest Neighbours. The intricacies of diabetes risk assessment in its early stages, however, were beyond the capabilities of these models. With an impressive 99.0% accuracy, the suggested ensemble model proved to be a powerful tool for combining the best features of SVM and Gradient Boosting. Compared to other models in the literature, our ensemble technique is more effective in detecting diabetes at an early stage. The research emphasises the significance of using sophisticated machine learning methods to improve clinical outcomes. These approaches may help diagnose issues associated to diabetes more quickly and accurately, which might reduce the burden on patients.
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Copyright (c) 2025 Shivani, Jayshree Boaddh, Meena Deshbhratar
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