Knowledge (Nov 2024)

Predictive Analytics for Thyroid Cancer Recurrence: A Machine Learning Approach

  • Elizabeth Clark,
  • Samantha Price,
  • Theresa Lucena,
  • Bailey Haberlein,
  • Abdullah Wahbeh,
  • Raed Seetan

DOI
https://doi.org/10.3390/knowledge4040029
Journal volume & issue
Vol. 4, no. 4
pp. 557 – 570

Abstract

Read online

Differentiated thyroid cancer (DTC), comprising papillary and follicular thyroid cancers, is the most prevalent type of thyroid malignancy. Accurate prediction of DTC is crucial for improving patient outcomes. Machine learning (ML) offers a promising approach to analyze risk factors and predict cancer recurrence. In this study, we aimed to develop predictive models to identify patients at an elevated risk of DTC recurrence based on 16 risk factors. We developed six ML models and applied them to a DTC dataset. We evaluated the ML models using Synthetic Minority Over-Sampling Technique (SMOTE) and with hyperparameter tuning. We measured the models’ performance using precision, recall, F1 score, and accuracy. Results showed that Random Forest consistently outperformed the other investigated models (KNN, SVM, Decision Tree, AdaBoost, and XGBoost) across all scenarios, demonstrating high accuracy and balanced precision and recall. The application of SMOTE improved model performance, and hyperparameter tuning enhanced overall model effectiveness.

Keywords