IMPROVING FUZZY CLUSTERING WITH MULTI-VERSE OPTIMIZER FOR CLASSIFYING RESPIRATORY DISEASES: INFLUENZA, COVID-19, AND PNEUMONIA
DOI:
https://doi.org/10.62985/j.huit_ojs.vol26.no3.443Keywords:
Fuzzy clustering, Multi-Verse Optimizer, Fuzzy C-Means, respiratory disease classification, medical diagnosisAbstract
Accurately classifying respiratory diseases such as influenza, COVID-19, and pneumonia is crucial for diagnosis and treatment. However, due to the similarity in symptoms, grouping patients into distinct categories presents significant challenges. This study proposes an improvement to the fuzzy clustering algorithm by integrating the Multi-Verse Optimizer (MVO) to optimize cluster center positions, enhancing clustering quality and reducing misclassification among disease groups. By combining MVO's global optimization capability with the FCM method, the model achieves more precise disease cluster separation. Experimental results demonstrate that the proposed method outperforms traditional FCM, significantly improving clustering accuracy and disease group differentiation. This approach has the potential to be applied in supporting respiratory disease diagnosis, contributing to increased efficiency in medical practice.
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