Optimized feature selection for diabetes prediction using IRIME
Lade...
Autor:in (Körperschaft)
Publikationsdatum
2026
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
2026 8th International Symposium on Computational and Business Intelligence (ISCBI)
Themenheft
DOI der Originalpublikation
Link
Zugehörige Forschungsdaten
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
214-221
Patentnummer
Verlag / Herausgebende Institution
IEEE
Verlagsort / Veranstaltungsort
Bali
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
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.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
2026 8th International Symposium on Computational and Business Intelligence (ISCBI)
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
06.02.2026
Enddatum der Konferenz
08.02.2026
Datum der letzten Prüfung
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
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
peer-reviewed
Open Access-Status
Closed
Zitation
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