Improved Kepler Optimization Algorithm for enhanced feature selection in liver disease classification.

Liver diseases represent a significant healthcare challenge, impacting millions globally and posing complexities in diagnosis. To address this global health concern, this paper introduces a groundbreaking enhancement to the Kepler Optimization Algorithm, termed I-KOA, designed specifically for feature selection in high-dimensional datasets. By harnessing the synergies of Opposition-Based Learning and a Local Escaping Operator grounded in the k-nearest Neighbor (kNN) classifier, I-KOA asserts itself as a potent tool for local exploitation, balanced exploration, and evasion of local optima. To our knowledge, this is the first work to exploit KOA as a feature selection method. Pioneering the utilization of KOA as a feature selection method, the paper rigorously tests I-KOA in two extensive experiments, tackling the complex CEC'22 benchmark suite functions and the intricate landscape of five liver disease datasets. Results underscore I-KOA's unparalleled performance, validated through the Friedman test, where it surpasses seven rival optimization algorithms. Achieving an outstanding overall classification accuracy of 93.46%, Feature selection size of 0.1042, sensitivity of 97.46%, precision of 94.37%, and F1-score of 90.35% across the liver disease datasets, I-KOA's randomized algorithm ensures robust feature selection, striking a compelling balance between subset size and classification efficacy. Acknowledging computational demands and generalization nuances, I-KOA is a formidable tool ready to revolutionize medical diagnosis and decision support systems. The open source codes of the proposed I-KOA are available at https://www.mathworks.com/matlabcentral/fileexchange/161376-improved-kepler-optimization-algorithm. • This paper encompasses a proficient I-KOA algorithm based on OBL and LEO methods. • A new optimized feature selection model for liver disease classification using five datasets. • We employ comprehensive analysis metrics to thoroughly assess the I-KOA algorithm's efficacy. • I-KOA stands out by surpassing its competitors, attesting to its remarkable performance. [ABSTRACT FROM AUTHOR]