# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 4 Methodology
### Feature Selection and ML Classification  
In the context of KD prediction, intelligently identifying the most informative features from the processed training data is crucial. This approach employs the ACO metaheuristic algorithm to extract key clinical features from the processed data. As explained in Section 3.2, the processed data is input into the ACO method, and the evaluation function assigns an importance score to each feature. Subsequently, the top-k thresholding method outlined in [28] is applied to identify the most significant features based on their importance scores. This method ranks features in descending order according to their importance scores derived from feature selection techniques. The top k features are then selected to form a refined subset, which is subsequently used to build a predictive model to enhance  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & **Features** & **Type** & **Description** & **Mean** & **Median** & **STD** \\ \hline
1 & Gender & String & Sex, either male or female & & & \\
2 & AgeBaseline & Numbers & Age in years & 53.20 & 54 & 13.82 \\
3 & HistoryDabetes & Binary & Patient with or without diabetes & & & \\
4 & HistoryCHID & Binary & Coronary heart disease (yes, no) & & & \\
5 & HistoryVascular & Binary & With or without vascular disease & & & \\
6 & HistorySmoking & Binary & Patient with or without smoking & & & \\
7 & HistoryHTN & Binary & History of hypertension & & & \\
8 & HistoryDLD & Binary & Patient with or without & & & \\  & & & dyslipidemia & & & \\
9 & HistoryObesity & Binary & Patient with or without obesity & & & \\
10 & DLDmeds & Binary & Dyslipidemia medication & & & \\
11 & DMmeds & Binary & Diabetes medication & & & \\
12 & HTNmeds & Binary & Hypertension medication & & & \\
13 & ACEIARB & Binary & History of ACEIARB use & & & \\
14 & CholesterolBaseline & Numeric & Cholesterol & 4.97 & 5 & 1.09 \\
15 & TriglyceridesBaseline & Numeric & Triglycerides & 1.31 & 1.11 & 0.79 \\
16 & HgbA1C & Numeric & Glycoslated hemoglobin & 6.59 & 6.10 & 1.71 \\
17 & CreatinineBaseline & Numeric & Creatinine & 67.85 & 66 & 17.91 \\
18 & eGFRBaseline & Numeric & Glomerular filtration rate & 98.11 & 98.05 & 18.50 \\
19 & sBPBaseline & Numeric & Systolic blood pressure & 131.37 & 131 & 15.69 \\
20 & dBPBaseline & Numeric & Diastolic blood pressure & 76.87 & 77 & 10.71 \\
21 & BMIBaseline & Numeric & Body mass index & 30.18 & 29.57 & 6.20 \\
22 & TimeToEventMonths & Numbers & Time information of events & 83.06 & 93 & 26.01 \\
23 & EventKD35 & Binary & Patient with KD-Yes or KD-No & & & \\ \hline \end{tabular}
\end{table}
Table 2: KD dataset features (STD: standard deviation).  
accuracy and computational efficiency. These selected features are then used for training ML classifiers, as illustrated in Figure 2.  
During the classification phase, multiple ML algorithms are trained and evaluated using the feature subset selected by ACO. Each classifier is trained using an 80:20 training-validation split, and its performance is optimized through k-fold cross-validation. The classifier achieving the highest performance is selected as the optimal model.