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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nawel Zemmal & Nacer Eddine Benzebouchi & Nabiha Azizi & Didier Schwab & Samir Brahim Belhaouari, 2022. "Unbalanced Learning for Diabetes Diagnosis Based on Enhanced Resampling and Stacking Classifier," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 18(1), pages 1-29, January.
    • Handle: RePEc:igg:jiit00:v:18:y:2022:i:1:p:1-29
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