# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 3 Background Information
### Machine Learning Algorithms  
In the medical field, ML algorithms are often used for clinical decision support and predictive analytics to help diagnose various diseases, including KD. Several previous research studies employed ML approaches for KD prediction. Frequently used algorithms include logistic regression, Adaboost, random forest, decision trees, extra gradient boosting, extra trees, and k-nearest neighbor classifiers [4; 5; 22; 27; 28].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Problem** & **Objective** & **Contribution** & **Experimental Results** \\ \hline \multirow{3}{*}{[21]} & KD symptoms are & To improve KD & Developed an ensemble & \multirow{3}{*}{\begin{tabular}{c} Achieved highest \\ performance with KNN, \\ CFS and Adaboost. \\ \end{tabular} } \\  & non-specific, making & & & \\ diagnosis challenging. & & & & \\ selection methods. & & & & \\ \hline \multirow{3}{*}{[22]} & Early KD detection is & To improve SVM & Applied wrapper and & \multirow{3}{*}{\begin{tabular}{c} Best performance \\ achieved with a filtered \\ subset evaluator. \\ \end{tabular} } \\  & required due to slow & classification of KD & filter approaches & & \\ disease progression. & using FS methods. & with SVM. & & \\ \hline \multirow{3}{*}{[23]} & IoT-based data analysis & To build an & Proposed FPA-DNN & \multirow{3}{*}{\begin{tabular}{c} The proposed approach \\ achieved good \\ experimental results. \\ \end{tabular} } \\  & to manage & IoT-based KD & & \\ \cline{1-1}  & high mortality. & diagnosis model for & pollination algorithm for & \\ \cline{1-1}  & & high performance. & DNN parameter tuning. & \\ \hline \multirow{3}{*}{[25]} & Identifying diet plans & Classification of diet & Developed dietary & \multirow{3}{*}{\
\begin{tabular}{c} Multiclass decision \\ forest achieved the \\ highest performance \\ \end{tabular} } \\  & for KD patients. & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{3}{*}{[26]} & Chronic KD prediction & Classification of KD & Developed an & \multirow{3}{*}{
\begin{tabular}{c} Achieved good results \\ interpretable ML \\ framework for \\ classifying KD patients. \\ \end{tabular} } \\  & using clinical and & & & \\ \cline{1-1}  & geographical data. & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of previous research approaches to diagnosing KD.  
In this paper, the extra trees (ET) classifier showed the best accuracy when used with ACO-selected features. The ET classifier is an ensemble learning technique widely used for classification tasks. It builds multiple decision trees by randomly selecting features and splitting points. This randomness often helps reduce overfitting and improves model generalization. ET is computationally efficient due to its simplified construction process. It has been shown to perform well with large datasets and high-dimensional features, making it suitable for complex medical datasets. Its robust handling of noisy data and ability to capture intricate relationships make it a preferred choice in many clinical decision-support systems.