Abstract
People in almost every part of the world struggle with a wide range of health issues, many of which require the application of various medical technology for the early diagnosis, treatment, and monitoring. Machine learning algorithms and deep learning techniques, both based on artificial intelligence, could potentially revolutionise medical diagnostics. This could be a huge step forward in the field. The primary objective of this study is to evaluate the performance of two different classifier models with regard to the diagnosis of thyroid problems. In order to do this, the models will be trained using data from the UCI repository. In this study, the accuracy and precision of the Convolutional Neural Network (CNN) and Support Vector Machine (SVM) algorithms are investigated in the context of the diagnosis of hypothyroidism and hyperthyroidism, respectively. The CNN classifier outperforms the SVM classifier with an accuracy of 89% and a precision of 87%, resulting in more accurate and consistent outcomes.
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Brindha, V., Muthukumaravel, A. (2023). Efficient Method for Predicting Thyroid Disease Classification using Convolutional Neural Network with Support Vector Machine. In: Joseph, F.J.J., Balas, V.E., Rajest, S.S., Regin, R. (eds) Computational Intelligence for Clinical Diagnosis. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-23683-9_6
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DOI: https://doi.org/10.1007/978-3-031-23683-9_6
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