Chronic Kidney Disease Classification Using Bagging and Particle Swarm Optimization Techniques

Chronic kidney disease (CKD) is a serious chronic illness without a definitive cure. According to WHO in 2015, 10% of the population suffers from CKD, with 1.5 million patients undergoing global haemodialysis. The incidence of CKD is increasing by 8% annually, ranking it as the 20th highest cause of global mortality. The Random Forest (RF) technique utilizes decision trees as an ensemble model, where class predictions are derived from the combination of results from each tree. The final decision is based on the highest outcome of class predictions generated by each decision tree, employed in this study. In testing, Random Forest with PSO-based Bagging achieved the highest performance with precision of 98.12%, recall of 100.00%, and AUC of 0.999. The Random Forest with PSO-based Bagging model demonstrates high performance in CKD detection, but metrics like precision, recall, and AUC alone do not guarantee clinical applicability. Balancing false positives and negatives is crucial, and its real-world integration should be evaluated to assess its impact on patient outcomes and clinical workflows. Research on predicting chronic kidney disease using the Random Forest algorithm with Bagging based on Particle Swarm Optimization (PSO) indicates that Bagging with PSO feature selection can enhance accuracy and kappa values. These findings contribute to understanding the roles of Bagging and PSO methods in improving the performance of several algorithms, including Random Forest.

@article{Irianto2025,
  title     = {Chronic Kidney Disease Classification Using Bagging and Particle Swarm Optimization Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {3},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Suhendro Y. Irianto and Dephi Linda and Immaniar I. M. Rizki and Sri Karnila and Dona Yuliawati},
  doi       = {10.14569/IJACSA.2025.0160368},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160368}
}