# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 6 Conclusions  
This paper presents an interpretable ML framework for predicting chronic kidney disease, incorporating metaheuristic ACO-based feature selection to enhance model performance. The ACO-based feature selection approach effectively improves KD prediction by identifying the most relevant clinical features, increasing diagnosis accuracy while reducing unnecessary tests. This is particularly beneficial in real-world medical settings, where minimizing diagnostic time and costs is critical. Multiple ML classifiers, including LR, RF, NB, DT, ET, and XGB, were evaluated, with ET achieving the highest accuracy of 97.7% and an AUC of 99.55% when using ACO-selected features.  
Beyond predicted accuracy, we improved the interpretability of the model by using SHAP and LIME approaches, providing both local and global explanations of decision-making processes. These enhance the model's reliability and clinical applicability by ensuring interpretability for medical practitioners. This approach has significant potential for integration with AI-powered clinical decision-support systems, enabling healthcare professionals to make more informed and accurate diagnoses. The approach is a vital addition to AI-driven healthcare, and its versatility implies that it might also be used to anticipate other medical conditions.  
Despite its benefits, the suggested ACO-based feature selection approach has a few limitations. First, ACO exhibits more computational complexity than conventional feature selection methods, potentially restricting its scalability for complex datasets. Second, the performance of ACO is highly dependent on hyperparameter optimization, requiring the diligent selection of parameters, such as the number of ants and the rate of pheromone decay. Although we only compared ACO and PSO, comparing them to other new metaheuristic optimizers would be beneficial. Finally, the model should be evaluated on various datasets to verify its generalizability and to prevent overfitting. Future studies should investigate the use of deep learning approaches to improve the model's adaptability and resilience in dealing with complex CKD patterns. The model's generalizability will be further confirmed by expanding the dataset to include a range of patient demographics. Moreover, real-time implementation in clinical environments and association with electronic health records (EHRs) might connect AI-driven diagnoses with practical medical applications. By solving these issues, future developments in AI-driven CKD diagnosis might greatly enhance early detection and patient outcomes, resulting in improved nephrology treatment.  
Conceptualization, A.J. and M.L.; methodology, A.J.; software, A.J.; validation, A.J. and M.L.; formal analysis, M.L.; investigation, M.L.; resources, M.L.; data curation, A.J.; writing--original draft preparation, A.J.; writing--review and editing, M.L.; visualization, A.J.; supervision, M.L.; project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the government (MSIT: Ministry of Science and ICT) (RS-2023-00321688).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the government (MSIT: Ministry of Science and ICT) (RS-2023-00321688).  
The authors declare no conflicts of interest.