## 3 Research Design and Procedure
### Feature Selection
Particle Swarm Optimization (PSO), Best First (BF), and Harmony Search (HS) are the three different search techniques used by CfsSubsetEval. These feature selection techniques were used to extensively examine and determine which ECG features were most helpful in CKD prediction. Every approach offers a different viewpoint on feature selection. PSO, as a population-based optimization strategy, allows dynamic feature subset creation; BF methodically adds or eliminates features to optimize a scoring function; and HS offers an element of unpredictability while repeatedly refining feature subsets. The objective was to thoroughly investigate the feature space, identify the most pertinent characteristics for improved CKD prediction with ECG data, reduce redundancy, and finally raise the accuracy and performance of the model. **a. Harmony Search:** CfsSubsetEval and the heuristic approach to optimization Attributes are chosen via Harmony Search (HS). It starts by increasing arbitrary feature subsets repeatedly. The consistency score (CS) (see Equation (5)), where the degree of redundancy in subset S is measured by TR(S) and the forecasting capacity is assessed by TP(S). Each cycle's new harmonies (characteristic subsets) are generated by combining the previously existing ones, with little squeezes made to accommodate variance. It is much saved if the harmonious arrangement improves the CS score. After a favorable number of rounds, this technique includes the United States while still meeting convergence standards. In the end, the set of rules selects the feature subset with the highest CS rating. **b. Best First Search**: Using CfsSubsetEval and Best First (BF) Search, feature selection is improved by repeatedly adding or removing features to increase the CS:  
Let S be an empty feature subset, to begin with. Find CS(S) by using the main function. Assess possible removals and additions of features: if a function is added, it will improve CS(S); if a feature is removed, it will complement CS(S), thus it will be removed from the subset. Until a halting requirement is satisfied, this procedure is repeated. In the end, choose the feature subset with the highest CS rating. TP(S) measures predictive power and TR(S) measures redundancy in the subset S as discussed in Equation (5). **c. Particle Swarm Optimization Search**: Particle Swarm Optimization (PSO) for at-tribute selection in CfsSubsetEval works by creating a population of feature subsets. Particles (which are subsets) adjust their locations (features) mathematically based on known coordinates and their velocity. To maximize the CS, TP and TR must be balanced (see Equation (5)):  
Particles update positions and velocities following:  
\[v\_i(t+1)=w\;*\;v\_i(t)+c1\;*\;rand1\;*\;(p\_best\_i-x\_i(t))+c2\;* \tag{8}\]  
\[rand2\;*\;(g\_best-x\_i(t)) \tag{9}\]  
\[x\_i(t+1)=x\_i(t)+v\_i(t+1) \tag{10}\]  
After iterations, select the feature subset with the highest CS score, optimizing attribute selection. Table 2 shows that all of these search methods found unique feature groups. We next utilized the finishing operation to choose the subset of features according to the majority vote method. We choose just those features that yield additional information about the purpose of the study and are selected by two or all three search strategies. The following is how this operation is expressed mathematically:  
* Let F represent the collection of all possible features. * F1, F2, and F3 are the feature subsets determined by the three distinct feature selection algorithms. * Fselected is the final subset of features selected using the majority vote method. The following is the mathematical equation for our ending operation:  
\[F_{selected}=\{f\in F\;||\;\{i\in\{1,2,3\}\;|\;f\in Fi\}\;|\geq 2\} \tag{11}\]  
By using your different feature selection strategies, this equation selects features from at least two of the three feature subsets. This is a majority vote method, meaning that the final subset has the characteristics selected by the majority of the approaches.