# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 1 Introduction  
Kidney disease (KD) significantly reduces the function of the kidneys in filtering waste and excessive fluid from the blood flow, resulting in severe health consequences. It is often associated with diabetes, hypertension, and glomerulonephritis, impacting millions of individuals and contributing to high morbidity and mortality rates worldwide [1, 2, 3]. KD often stays asymptomatic in its early stages, making it an undetected significant factor in mortality and increasing the risk of disease progression to advanced stages. Once KD reaches a critical stage, treatment options become limited, often requiring dialysis or kidney transplantation. Early diagnosis and intervention are crucial in preventing irreversible kidney damage. Although traditional diagnostic techniques can confirm the existence of KD, they are ineffective in predicting its progression and response to treatment [4, 5, 6]. This limitationunderscores the need for advanced predictive methodologies to improve early diagnosis and enhance clinical decision-making. Machine learning (ML) has gained significant attention for KD prediction because it analyzes complex clinical data and extracts meaningful patterns. The feature selection (FS) data preprocessing phase plays an important role in ML-based predictive models for KD. FS methods select optimal feature subsets while balancing relevance and are thus vital to improving classification accuracy. However, FS methods often involve significant challenges, such as determining optimal objective functions to guide the selection process, dealing with the computational complexity involved in evaluating feature subsets, and managing the trade-off between the number of selected features and the overall model performance. With the vast number of possible feature subsets, the computational complexity of FS is high. It is an NP-hard combinatorial optimization problem. Exhaustive search methods are computationally infeasible, while traditional approaches such as finding an optimal characteristic set (FOCUS) often fail to produce satisfactory results. Metaheuristic algorithms, particularly those inspired by swarm intelligence and evolutionary strategies, have been introduced to tackle the problem. Although these algorithms show promising results in optimizing feature selection while balancing accuracy and computational efficiency [6; 7], many suffer from stagnation in sub-optimal solutions, leading to reduced classification accuracy and subpar feature subset selection [8; 9]. While contemporary optimizers such as the red fox optimizer, crayfish optimization algorithm, and reptile search algorithm have been introduced, no single optimization algorithm universally outperforms others across all problems ([PERSON]'s no-free-lunch theorem) [10; 11; 12]. Therefore, an optimizer suitable for FS in KD prediction must be chosen to efficiently explore the search space, adapt dynamically to feature relevance, and avoid premature convergence. This paper proposes an enhanced automated framework for KD prediction that integrates ant colony optimization (ACO) metaheuristic algorithms for feature selection to refine predictive accuracy while minimizing computational complexity. The AOC balances exploration and exploitation while effectively handling high-dimensional datasets [13]. Furthermore, eXplainable AI (XAI) techniques are incorporated to enhance model interpretability, making the predictions more transparent and clinically useful. Unlike conventional approaches, which often struggle with high computational overheads, the proposed framework efficiently identifies the most significant clinical features, balancing performance and interpretability. Key contributions of this paper are as follows:  
* An optimized ACO-based feature selection approach is applied to extract the most relevant clinical features, reducing model complexity while maintaining high predictive accuracy.