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One of the most common cancers in the world, thyroid cancer is becoming more common. Improving patient outcomes requires precise thyroid cancer prediction and early identification. The creation of prediction models based on a variety of patient data is now possible thanks to machine learning techniques, which have become highly effective instruments for medical diagnostics. This study uses machine learning algorithms to propose a novel method of predicting thyroid cancer. We gathered a large dataset from a cohort of patients with thyroid cancer that included clinical, demographic, and imaging data. We developed prediction models for thyroid cancer risk assessment by utilizing this data and a variety of machine learning algorithms, such as decision trees, support vector machines, random forests, and logistic regression. The findings of our investigation show how well these machine learning algorithms predict thyroid cancer. Our models show promise for accurate risk assessment and early diagnosis due to their high sensitivity and specificity. In order to determine the most important variables influencing the risk of thyroid cancer, we also carried out feature selection and engineering. This helped us uncover prospective biomarkers and risk factors. This study can help medical practitioners make well-informed decisions about patient care and has great promise for the diagnosis of thyroid cancer. By identifying high-risk patients and assisting physicians in offering prompt therapies, machine learning can be integrated into the prediction of thyroid cancer, ultimately improving patient outcomes and lessening the burden of this common malignancy.
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Copyright © 2026, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: MOLLI SRINIVASA RAO, drmollisrinivasarao@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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