Principal component analysis (PCA) is a data-driven and multivariate approach that is widely used for dimensionality reduction and pattern detection in various fields [24–26]. PCA projects original data onto mutually orthogonal principal axes while preserving as much of the variance as possible. The resulting principal components (PCs) are a weighted combination of the original variables, with the first principal component (PC1) explaining the largest amount of variance, the second principal component (PC2) explaining the second largest amount, and so forth. In this way, the extracted PCs capture the interrelation of all the original variables, revealing hidden and simplified structures that underlie complex data [24, 26].
