In recent years, there has been an increase in thyroid disorders. Early thyroid illness identification is a crucial endeavor because of its significance in metabolism. Although there are numerous studies on the identification of whether thyroid illness leads to cancer or not, in order to properly address and handle this condition, a timely and accurate medical diagnosis is crucial. Thyroid cancer detection and diagnosis could be enhanced by algorithms that utilize machine learning, which have achieved considerable amounts of interest especially in the medical field. This paper describes how machine learning techniques are being used to diagnose thyroid cancer. K-Nearest Neighbor, Decision Tree, Random orest, Ada-Boost with decision tree classifier, Gradient Boosting Classifier, Stochastic Gradient Boosting classifier, Extended Gradient Boosting Classifier, Extended Gradient Boosting Classifier with hyper parameter tuning, Extra Tree Classifier, Light GBM Classifier, Voting Classifier are among the machine learning approaches that are used and evaluated to assess how well they diagnose thyroid Cancer. The study evaluates the methods precision, recall, F1-score, and Accuracy. From all the above machine learning models Random Forest, Ada Boost, Extended gradient boosting classifier, Extra Tree Classifier, Light Gradient Boosting Machine outperforms the other models.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Kalpana K Harish and Kuppala Saritha;
Methodology: Kalpana K Harish;
Software: Kuppala Saritha;
Data Curation: Kalpana K Harish;
Writing- Original Draft Preparation: Kalpana K Harish and Kuppala Saritha;
Visualization: Kalpana K Harish;
Investigation: Kuppala Saritha;
Supervision: Kalpana K Harish;
Validation: Kuppala Saritha;
Writing- Reviewing and Editing: Kalpana K Harish and Kuppala Saritha;
All authors reviewed the results and approved the final version of the manuscript.
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I sincerely thank Dr. Syed Siraj Ahmed for his valuable support and generous contribution of time in completing this paper.
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Kuppala Saritha
School of Information Science, Presidency University, Bengaluru, Karnataka, India.
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Cite this article
Kalpana K Harish and Kuppala Saritha, “Optimized Classification and Prediction with Hybrid Machine Learning Models”, Journal of Machine and Computing, vol.6, no.1, pp. 220-231, 2026, doi: 10.53759/7669/jmc202606016.
