{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T05:20:56Z","timestamp":1766035256285,"version":"3.48.0"},"reference-count":47,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>The rapid expansion of medical data, characterized by its complex high-dimensional attributes, presents numerous promising opportunities and substantial challenges in healthcare analytics. Adopting effective feature selection techniques is essential to take advantage of the potential of such data. This research presents a modified algorithm called (mDA), which is the hybrid algorithm between the Evolutionary Population Dynamics and the Dragonfly Algorithm. This method combines Evolutionary Population Dynamics\u2019s strength with the Dragonfly Algorithm\u2019s flexible capabilities, offering a robust evolutionary machine learning approach specifically designed for medical data analysis. By integrating the dynamic population modeling of Evolutionary Population Dynamics with the adaptive search techniques of Dragonfly Algorithm, the proposed mDA significantly improves accuracy, reduces the number of features, and obtains the minimum average of the fitness scores. Comparative experiments conducted on seven diverse medical datasets against other established algorithms confirm the superior performance of the proposed mDA, establishing it as a valuable approach in examining complex medical data.<\/jats:p>","DOI":"10.3390\/computation13120292","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T10:11:23Z","timestamp":1765793483000},"page":"292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["mDA: Evolutionary Machine Learning Algorithm for Feature Selection in Medical Domain"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9265-9819","authenticated-orcid":false,"given":"Ibrahim","family":"Aljarah","sequence":"first","affiliation":[{"name":"The Department of Artificial Intelligence, The University of Jordan, Amman 11942, Jordan"}]},{"given":"Abdullah","family":"Alzaqebah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The World Islamic Sciences and Education University, Amman 11947, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0980-7559","authenticated-orcid":false,"given":"Nailah","family":"Al-Madi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman 11941, Jordan"}]},{"given":"Ala\u2019 M.","family":"Al-Zoubi","sequence":"additional","affiliation":[{"name":"Department of Data Science and Artificial Intelligence, Faculty of Science and Information Technology, Al-Zaytoonah University of Jordan, Amman 11733, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8444-7309","authenticated-orcid":false,"given":"Amro","family":"Saleh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman 11941, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.eswa.2018.03.024","article-title":"Improving Extreme Learning Machine by Competitive Swarm Optimization and its application for medical diagnosis problems","volume":"104","author":"Eshtay","year":"2018","journal-title":"Expert Syst. 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