# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 2 Previous Research  
Several ML-based solutions have recently been proposed for the prediction and detection of KD. In this section, previous research studies that utilize feature selection approaches are first reviewed, and then the relevant state-of-the-art techniques for KD prediction and detection are stated. Feature selection techniques are divided into local and global categories. Global approaches identify the optimal features from the entire solution space, while local methods concentrate on the best features within a specific neighborhood of the feature space [14]. Metaheuristic algorithms are often used in both population-centric and neighborhood-centric optimization. Examples include genetic algorithms [15], ACO [16], binary gray wolf optimization [17], the butterfly optimization algorithm [18], particle swarm optimization (PSO) [19], etc. These algorithms effectively identify optimal feature subsets, balancing relevance and computational efficiency using global and local optimization methodologies [17]. A novel FS approach based on the firefly algorithm (FA) was proposed by [18] to reduce redundant features in high-dimensional medical datasets. The improved FA outperformed existing techniques in COVID-19 patient prediction by integrating genetic operators and quasi-reflexive-based learning. The hybrid nature of this approach demonstrates the potential of swarm intelligence in optimizing feature selection for medical disease diagnosis. A recent study [20] introduced a binary moth-flame optimization (B-MFO) approach for FS in medical datasets, addressing the limitations of existing binary metaheuristic algorithms. The study proposed three B-MFO variants using S-shaped, V-shaped, and U-shaped transfer functions to enhance scalability and effectiveness. Evaluated on seven medical datasets, B-MFO outperformed well-known binary metaheuristic algorithms in feature selection performance. Some relevant state-of-the-art techniques for the prediction and detection of KD are as follows:  
* [PERSON] et al. [15] proposed a hybrid classification framework for chronic kidney disease (CKD), utilizing ACO, GA, and PSO. The study showed that incorporating feature selection enhances classification performance, with ACO-FS outperforming GA-FS and PSO-FS when tested on a benchmark CKD dataset. LR was used as the final classifier, highlighting the effectiveness of FS methodologies in CKD prediction. * [PERSON] et al. [17] introduced a novel CKD framework integrating binary gray wolf optimization (FS) and an extreme learning machine (ELM) for classification. The approach emphasizes the use of preprocessing techniques for handling missing values and optimizing data for deep analysis. Experimental results on a benchmark CKD dataset showed superior accuracy compared with existing models, highlighting the effectiveness of metaheuristic-driven FS in improving CKD diagnosis. * [PERSON] et al. [21] used a hybrid approach by combining k-nearest neighbors and Adaboost, and the correlation-based feature selection approach for selecting 17 features from 24, reaching a 98.1% accuracy. Similarly, [PERSON] et al. [22] employed a support vector machine (SVM) classifier with a filter subset evaluator to select features. Thirteen significant characteristics were determined from a total of 24, and an accuracy of 98.5% was achieved. [PERSON] et al. [23] presented a deep neural network (DNN) model using the flower pollination algorithm, which exhibited enhanced performance in various evaluation metrics. They processed the data and selected the key features using the oppositional crow search algorithm. They showed significant improvements over previous approaches by achieving a 98.75% accuracy. Although these models achieve high prediction accuracy, mainly by using feature selection techniques, they lack interpretability, which is essential for clinical adoption. * [PERSON] et al.