Optimized feature selection for diabetes prediction using IRIME
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Publication date
2026
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04B - Conference paper
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2026 8th International Symposium on Computational and Business Intelligence (ISCBI)
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Issue / Number
Pages / Duration
214-221
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IEEE
Place of publication / Event location
Bali
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Abstract
Early prediction of diabetes is critical for preventive care, but modern datasets often contain many features that can hinder model performance through overfitting and increased complexity. In this paper, we investigate the effectiveness of IRIME, an improved version of RIME which is a novel computation intelligence algorithm inspired by rime-ice, for optimized feature selection in diabetes prediction. We compare IRIME's performance against using all available features and against a custom ensemble feature selection model. This ensemble aggregates the outputs of several computational intelligence algorithms, including Particle Swarm Optimization, Genetic Algorithm, Differential Evolution, Ant Colony Optimization, Brain Storm Optimization, Bat algorithm, and Binary PSO. We apply these methods to a real-world Iraqi diabetes dataset with 1000 patients, using LightGBM and Multilayer Perceptron classifiers to evaluate each selected feature subset. Our results show that the IRIME-selected features achieve an F1-score of 0.990, nearly matching the performance of using all features (F1 ≈0.992) and the custom ensemble model (F1≈0.989) while dramatically reducing the feature count. We interpret the contributions of the selected features using Shapley Additive Explanations to provide insight into their impact on the model's predictions. The IRIME algorithm demonstrates its effectiveness in finding a highly informative feature subset, performing comparably to or even surpassing the custom ensemble and other individual feature selectors in predictive performance. This work demonstrates an efficient, interpretable feature selection framework that improves diabetes prediction and could reduce diagnostic tests.
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Event
2026 8th International Symposium on Computational and Business Intelligence (ISCBI)
Exhibition start date
Exhibition end date
Conference start date
06.02.2026
Conference end date
08.02.2026
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ISBN
979-8-3315-5080-6
979-8-3315-5079-0
979-8-3315-5081-3
979-8-3315-5079-0
979-8-3315-5081-3
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Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
peer-reviewed
Open access category
Closed
Citation
Damilakis, E., Soni, H., Dornberger, R., & Hanne, T. (2026). Optimized feature selection for diabetes prediction using IRIME. 2026 8th International Symposium on Computational and Business Intelligence (ISCBI), 214–221. https://doi.org/10.1109/iscbi69404.2026.11496039