<![CDATA[Diabetic Nephropathy (DN) is a critical complication with a mortality rate 20-40 times higher than in non-diabetic nephropathy patients, necessitating precise machine learning models to determine whether a patient has nephropathy. Extreme Gradient Boosting (XGBoost) has emerged as a prominent machine learning model for medical diagnostics, with several studies validating its superiority in medical classification. Nevertheless, a significant limitation of XGBoost lies in the complexity of manual hyperparameter tuning. To address this limitation, an automated optimization algorithm is requisite to systematically identify the optimal hyperparameter configuration. This study focuses on optimizing Extreme Gradient Boosting (XGBoost) hyperparameters using Particle Swarm Optimization (PSO), with the F1-Score as its fitness function. To evaluate its effectiveness, the performance of this hybrid XGBoost-PSO model was compared against the baseline XGBoost model. The results showed that the hybrid model outperformed the baseline model, achieving a consistent improvement of 0.02 (2%) across all evaluation metrics. Notably, the F1-Score increased from 0.91 to 0.93, while the Recall metric improved from 0.93 to 0.95. Furthermore, the PSO algorithm efficiently identified the Global Best (GBest) hyperparameters at the 9th iteration. In conclusion, the XGBoost-PSO model provides a robust medical diagnostic tool that maintains a stable performance to enhance clinical judgment.]]>
Copyrights © 2025
