Gil-Rios, Miguel-AngelMiguel-AngelGil-RiosCruz-Aceves, IvanIvanCruz-AcevesHernandez-Aguirre, ArturoArturoHernandez-AguirreMoya-Albor, ErnestoErnestoMoya-AlborBrieva, JorgeJorgeBrievaHernandez-Gonzalez, Martha-AliciaMartha-AliciaHernandez-GonzalezSolorio-Meza, Sergio-EduardoSergio-EduardoSolorio-Meza2024-02-292024-02-292024Gil-Rios, M.-A., Cruz-Aceves, I., Hernandez-Aguirre, A., Moya-Albor, E., Brieva, J., Hernandez-Gonzalez, M.-A., & Solorio-Meza, S.-E. (2024). High-Dimensional Feature Selection for Automatic Classification of Coronary Stenosis Using an Evolutionary Algorithm. In Diagnostics (Vol. 14, Issue 3, p. 268). MDPI AG. https://doi.org/10.3390/diagnostics14030268https://scripta.up.edu.mx/handle/20.500.12552/994610.3390/diagnostics14030268In this paper, a novel strategy to perform high-dimensional feature selection using an evolutionary algorithm for the automatic classification of coronary stenosis is introduced. The method involves a feature extraction stage to form a bank of 473 features considering different types such as intensity, texture and shape. The feature selection task is carried out on a high-dimensional feature bank, where the search space is denoted by O(2𝑛) and 𝑛=473. The proposed evolutionary search strategy was compared in terms of the Jaccard coefficient and accuracy classification with different state-of-the-art methods. The highest feature selection rate, along with the best classification performance, was obtained with a subset of four features, representing a 99%99% discrimination rate. In the last stage, the feature subset was used as input to train a support vector machine using an independent testing set. The classification of coronary stenosis cases involves a binary classification type by considering positive and negative classes. The highest classification performance was obtained with the four-feature subset in terms of accuracy (0.86)(0.86) and Jaccard coefficient (0.75)(0.75) metrics. In addition, a second dataset containing 2788 instances was formed from a public image database, obtaining an accuracy of 0.890.89 and a Jaccard Coefficient of 0.800.80. Finally, based on the performance achieved with the four-feature subset, they can be suitable for use in a clinical decision support system. ©© 2024 by the authors. Licensee MDPI, Basel, Switzerland. MDPIenBank of featuresCoronary angiogramsEvolutionary algorithmFeature selectionK-nearest neighborStenosis classificationResource Types::text::journal::journal article