mDA: Evolutionary Machine Learning Algorithm for Feature Selection in Medical Domain

Ibrahim Aljarah

doi:10.3390/computation13120292

mDA: Evolutionary Machine Learning Algorithm for Feature Selection in Medical Domain

2025-12-13

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DOI：10.3390/computation13120292

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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's strength with the Dragonfly Algorithm's 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.

Keywords:

feature selection

dragonfly algorithm

optimization

medical data analytics

evolutionary algorithms

classification

mDA: Evolutionary Machine Learning Algorithm for Feature Selection in Medical Domain

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