Clark, E.; Price, S.; Lucena, T.; Haberlein, B.; Wahbeh, A.; Seetan, R. Predictive Analytics for Thyroid Cancer Recurrence: A Machine Learning Approach. Preprints2024, 2024090667. https://doi.org/10.20944/preprints202409.0667.v1
APA Style
Clark, E., Price, S., Lucena, T., Haberlein, B., Wahbeh, A., & Seetan, R. (2024). Predictive Analytics for Thyroid Cancer Recurrence: A Machine Learning Approach. Preprints. https://doi.org/10.20944/preprints202409.0667.v1
Chicago/Turabian Style
Clark, E., Abdullah Wahbeh and Raed Seetan. 2024 "Predictive Analytics for Thyroid Cancer Recurrence: A Machine Learning Approach" Preprints. https://doi.org/10.20944/preprints202409.0667.v1
Abstract
Differentiated thyroid cancer (DTC), comprising of 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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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