Abstract: Algorithms for feature selection are growing in interest among researchers aiming to connect specific features in a dataset with specific classifications. Recent developments in machine learning, particularly Support Vector Machine-based artificial intelligence algorithms have demonstrated excellent classification performance in highly nonlinear data. However, identifying which features contribute most to classification re-mains challenging, especially when datasets include hundreds of variables. Initially, features must be screened to narrow down the set for deeper analysis. Metabolomics datasets are one such case, where many features must be examined to determine those associated with heart disease diagnosis. This work applies a Genetic Algorithm, incorporating a penalized likelihood approach with Support Vector Machines for mutation, to stochastically search the feature space. A large-scale simulation study demonstrates that the proposed method achieves a high true feature identification rate while maintaining a reasonable false identification rate. The method is then applied to a Qatar BioBank dataset focused on heart disease, reducing the number of candidate metabolites from 232 to 37.

Edward L. Boone, Ryad A. Ghanam, Faten S. Alamri and Elizabeth B. Amona, “Metabolite Screening for Heart Disease Using Support Vector Machine-Based AI” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160606

@article{Boone2025,
title = {Metabolite Screening for Heart Disease Using Support Vector Machine-Based AI},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160606},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160606},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Edward L. Boone and Ryad A. Ghanam and Faten S. Alamri and Elizabeth B. Amona}
}