Unbalanced Learning for Diabetes Diagnosis Based on Enhanced Resampling and Stacking Classifier

Nawel Zemmal, Nacer Eddine Benzebouchi, Nabiha Azizi, Didier Schwab and Samir Brahim Belhaouari
Additional contact information
Nawel Zemmal: Mathematics and Computer Science Department, Mohamed Cherif Messaadia University, Souk-Ahras, Algeria & Labged Laboratory, Badji Mokhtar Annaba University, Annaba, Algeria
Nacer Eddine Benzebouchi: Labged Laboratory, Computer Science Department, Badji Mokhtar Annaba University, Annaba, Algeria
Nabiha Azizi: Labged Laboratory, Computer Science Department, Badji Mokhtar Annaba University, Annaba, Algeria
Didier Schwab: Grenoble Alpes University, Grenoble, France
Samir Brahim Belhaouari: College of Science and Engineering, Doha, Qatar

International Journal of Intelligent Information Technologies (IJIIT), 2022, vol. 18, issue 1, 1-29

Abstract: Diabetes is characterized by an abnormally enhanced concentration of glucose in the blood serum. It has a damaging impact on several noble body systems. Today, the concept of unbalanced learning has developed considerably in the domain of medical diagnosis, which greatly reduces the generation of erroneous classification results. The paper takes a hybrid approach to imbalanced learning by proposing an enhanced multimodal meta-learning method called IRESAMPLE+St to distinguish between normal and diabetic patients. This approach relies on the Stacking paradigm by utilizing the complementarity that may exist between classifiers. In the same focus of this study, a modified RESAMPLE-based technique referred to as IRESAMPLE+ and the SMOTE method are integrated as a preliminary resampling step to overcome and resolve the problem of unbalanced data. The suggested IRESAMPLE+St provides a computerized diabetes diagnostic system with impressive results, comparing it to the principal related studies, reflecting the design and engineering successes achieved.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIIT.309583 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jiit00:v:18:y:2022:i:1:p:1-29

Access Statistics for this article

International Journal of Intelligent Information Technologies (IJIIT) is currently edited by Vijayan Sugumaran

More articles in International Journal of Intelligent Information Technologies (IJIIT) from IGI Global
Bibliographic data for series maintained by Journal Editor ().