Artificial Intelligence Fights Thyroid Cancer: Accurate Prediction of Thyroid Cancer Recurrence Risk through Machine Learning Models

Abstract

The aim of this study is to determine the possible use of machine learning models in improving predictive accuracy of recurrence of thyroid cancer, particularly Differentiated Thyroid Cancer (DTC). A cohort of 383 patients who underwent radioactive iodine treatment was studied with six machine learning models, i.e., Random Forest, Decision Tree, and K-Nearest Neighbors. Experiments showed that Random Forest was the best-performing model, which had 98.7% test set accuracy, showing that it had strong predictive power with accuracy and reliability. Data rebalancing solved data imbalance issues, as well as the utilization of methods for hyperparameter tuning. Feature selection and the use of laboratory and clinical data in improving performance were also a high priority in the research. But more research will be required to enhance clinical transparency and develop models on more heterogeneous data, incorporating genetic and biochemical biomarkers.

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