Abstract: This study explores thyroid disease detection using K-Means and Fuzzy C-Means clustering algorithms. By analyzing patient data, the models classify thyroid conditions efficiently. Comparative evaluation highlights accuracy and effectiveness, aiding early diagnosis. The research emphasizes the importance of machine learning in medical diagnostics, enhancing predictive capabilities for thyroid disorders. The data is first pre- processed, selected, extracted and then classified defect or normal class. This algorithm gives best result for overlapped data also. Existing studies focus on the binary classification tasks. This study improves on prior experience with conventional CNN models (i.e., VGG models), the more advanced CNN architecture, the Exception model was implemented and compared to achieve the automatic diagnosis with increased efficiency and accuracy. It is also likely that the established structure can be easily translated to determine the diagnosis of other disease
Keywords: Thyroid Disease, Data Mining, Fuzzy C Means Clustering.
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DOI:
10.17148/IJARCCE.2025.14666
