A Weighted Fuzzy-Based Model for Thyroid Prediction in Distributed Environment
DOI:
https://doi.org/10.63682/jns.v14i23S.5757Keywords:
thyroid, mining approaches, prediction, accuracy, evaluationAbstract
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
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Begum A, Parkavi A. Prediction of thyroid disease using data mining techniques in 2019 5th International conference on advanced computing and communication systems ICACCS 2019; 2019 pp. 342–345.
Zhu Y, Fu Z, Fei J. An image augmentation method using the convolutional network for thyroid nodule classification by transfer learning. In: 2017 3rd IEEE International Conference on Computer Communications ICCC 2017; 2017, vol. 2018-Janua, pp. 1819–1823.
Ilyas M, et al. Deep learning based classification of thyroid cancer using different medical imaging modalities: a systematic review. VFAST Trans Softw Eng. 2021
Begum A, Parkavi A. Prediction of thyroid disease using data mining techniques. In: 2019 5th International Conference on Advanced Computing and Communication Systems ICACCS 2019, no. August 2016; 2019, pp. 342–345.
Ahmed I, et al. Lithium from breast milk inhibits thyroid iodine uptake and hormone production, which are remedied by maternal iodine supplementation. Bipolar Disord. 2021;23(6):615–25.
Priyadharsini D, Sasikala S. Efcient thyroid disease prediction using features selection and meta-classifiers. In: Proceedings—6th International Conference on Computing Methodologies and Communication ICCMC 2022, no. ICCMC; 2022, pp. 1236–1243
Ahmed I, Mohiuddin R, Muqeet MA, Kumar JA, Thaniserikaran A. Thyroid cancer detection using deep neural network. In: Proceedings— International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022, no. ICAAIC; 2022, pp. 166–169
Jamil M, Wlvvxh WKU. Ensemble-based effective diagnosis of thyroid disorder with various feature selection techniques; 2022, pp. 14–19..
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