A Weighted Fuzzy-Based Model for Thyroid Prediction in Distributed Environment - Amutha C, Amutha C; P. Periyasamy, P. Periyasamy (Journal of Neonatal Surgery, 2025)
Thyroid disorder is the most widespread endocrine disorders globally. Thyroid gland plays a crucial role in regulating human metabolism, its dysfunction poses significant health concerns. An automated, dependable, and precise machine learning (ML) system for thyroid disease identification is essential to improve diagnostic efficiency and minimize errors. The proposed model seeks to address several limitations in existing approaches, including insufficient feature analysis, inadequate visualization, and the need for enhanced prediction accuracy and reliability. This study utilizes a publicly available thyroid infected dataset from the UCI-ML repository, which includes twenty-nine clinical features. These features were instrumental in developing a weighted fuzzy model capable of predicting thyroid disease by evaluating initial signs and symptoms and eliminating the need for manual assessment of these characteristics. The feature assessment and classification help in identifying the contribution of each feature to thyroid disease prediction. To overcome the issue of over-fitting, the model employs data normalization. The use of weighted fuzzy further strengthens the system's reliability by evaluating multiple classifiers in the decision-making process. The suggested framework achieved an impressive sensitivity of 99%, accuracy of 99.5%, and specificity of 99.9% demonstrating it’s potential for real-time CAD systems, thereby facilitating timely recognition and appropriate treatment in its early stage