## 4 Proposed EHMFFL Algorithm
### Feature Selection Using PCC-GWO  
Feature selection is a crucial step in machine learning, particularly when dealing with datasets with high dimensionality. Its primary objective is to streamline the dataset by reducing its dimensionality, thereby identifying the most relevant features that contribute significantly to predictive accuracy, while discarding irrelevant or noisy attributes. This process not only enhances computational efficiency, but also minimizes redundancy among the selected features. Feature selection is essential in various domains, including text categorization, data mining, pattern recognition, and signal processing [33], where it aids in improving model performance by focusing on the most informative attributes and discarding superfluous ones. Feature selection poses a challenging problem, acknowledged as non-deterministic polynomial hard (NP-hard) [34], making exact (exhaustive) search methods impractical due to their computational complexity and time requirements. Therefore, heuristic and metaheuristic algorithms and their hybridizations become essential in this context [35]. When crafting a metaheuristic algorithm for an NP-hard problem, a delicate balance between exploration and exploitation must be carefully maintained to optimize search algorithms [36]. The GWO is recognized in the literature for its adeptness in striking the right equilibrium between exploration and exploitation. Simultaneously, the PCC stands out as a swift heuristic method for identifying and eliminating highly correlated features [37]. Hence, we have chosen to employ PCC and GWO as the heuristic and metaheuristic components of our integrated PCC-GWO feature selection algorithm. This strategy aims to harness the advantages of both methods concurrently, combining the speed of heuristic-based PCC with the precision of metaheuristic-driven GWO to enhance the feature selection process. Algorithm 1 outlines the PCC-GWO feature selection approach, offering a hybrid method for selecting an optimal feature subset for each base learner within the ensemble learning model. Initially, the algorithm employs the PCC method to compute an importance score for each feature. Subsequently, these scores serve as heuristic knowledge to guide the GWO during the search process. To achieve this, the importance scores are normalized within the range of [0, 1], and a roulette wheel selection method is utilized to choose features for each grey wolf within the initial population generation procedure. The subsequent sections delve into the specifics of the PCC-GWO algorithm, encompassing both the PCC and GWO phases, facilitating a comprehensive understanding of the feature selection process. ```
1:Full heart disease dataset Output: Optimal Feature Subset for Machine Learning Model Heuristic Feature Selection: Calculation of Importance Scores using PCC:
2:For(\(i=1\): Number of Features)
3:Calculation of the correlation of feature \(i\) with the class: CC\({}_{i}\)
4:Calculation of the correlation of feature \(i\) in relation to the other features: \(CF_{i}\)
5:Calculation of the PCC importance score of feature \(i\): \(IS_{i}=\text{CC}_{i}/\text{CC}_{i}\)
6:End For Metaheuristic Feature Selection: Final Feature Subset Selection using GWO:
7:\(t=0\) (Initial Population)
8:For(\(s=1\): PopSize)
9:for(\(i=1\): Number of Features)
10: Calculation of the probability of feature \(i\) in solution \(s\) using Equation (3)
11: Deciding to select or decline feature \(i\) using roulette wheel selection
12:endfor
13:Calculation of the fitness of each grey wolf \(s\) using Equation (4)
14:End For
15:Considering the best solution as alpha wolf: \(X_{\alpha}\)
16:Considering the second best solution as beta wolf: \(X_{\beta}\)
17:Considering the third best solution as delta wolf: \(X_{\delta}\)
18:For(\(t=1\): MaxIter) % Population Updating
19:for each grey wolf \(s\)
20:Updating \(a\), \(A_{ij}\), and \(C_{i}\), \(r_{Ai}\), and \(r_{Ci}\).