ArticleScientific reports2020
Machine Learning and Feature Selection Applied to SEER Data to Reliably Assess Thyroid Cancer Prognosis.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- A comparative study on advanced predictive modeling of thyroid cancer recurrence using multi algorithmic machine learning frameworks.Scientific reports · 2025Article
- TC check: a web app for thyroid cancer recurrence prediction using explainable machine learning.Journal of cancer research and clinical oncology · 2025Article
- CatBoost with physics-based metaheuristics for thyroid cancer recurrence prediction.BioData mining · 2025Article
- Biomarker identification through spatial proteomics for the characterization of indeterminate thyroid nodules.Endocrine · 2025Article
- Advanced machine learning framework for thyroid cancer epidemiology in Iran through integration of environmental socioeconomic and health system predictors.Scientific reports · 2025Article
- Post-operative prognostication of patients diagnosed with Hurthle cell carcinoma: a machine learning approach.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Machine Learning for Thyroid Cancer Detection, Presence of Metastasis, and Recurrence Predictions-A Scoping Review.Cancers · 2025Review
- Predicting overall survival in anaplastic thyroid cancer using machine learning approaches.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- A machine learning model for predicting bone and/or lung metastasis in differentiated thyroid carcinoma: enhancing precision in risk stratification.Frontiers in endocrinology · 2025Article
- DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation.BMC medical informatics and decision making · 2024Article
- Development and validation of prediction models for papillary thyroid cancer structural recurrence using machine learning approaches.BMC cancer · 2024Article
- Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis.Scientific reports · 2024Article
- Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques.Scientific reports · 2024Article
- Application of a Novel Multimodal-Based Deep Learning Model for the Prediction of Papillary Thyroid Carcinoma Recurrence.International journal of general medicine · 2024Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- A Deep Learning Framework for the Characterization of Thyroid Nodules from Ultrasound Images Using Improved Inception Network and Multi-Level Transfer Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Learning dispatching rules via novel genetic programming with feature selection in energy-aware dynamic job-shop scheduling.Scientific reports · 2023Article
- Long-term survival and second malignant tumor prediction in pediatric, adolescent, and young adult cancer survivors using Random Survival Forests: a SEER analysis.Scientific reports · 2023Article
- Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics.Journal of personalized medicine · 2021Article
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9 authors.
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Abstract
Utilizing historical clinical datasets to guide future treatment choices is beneficial for patients and physicians. Machine learning and feature selection algorithms (namely, Fisher's discriminant ratio, Kruskal-Wallis' analysis, and Relief-F) have been combined in this research to analyse a SEER database containing clinical features from de-identified thyroid cancer patients. The data covered 34 unique clinical variables such as patients' age at diagnosis or information regarding lymph nodes, which were employed to build various novel classifiers to distinguish patients that lived for over 10 years since diagnosis, from those who did not survive at least five years. By properly optimizing supervised neural networks, specifically multilayer perceptrons, using data from large groups of thyroid cancer patients (between 6,756 and 20,344 for different models), we demonstrate that unspecialized and existing medical recording can be reliably turned into power of prediction to help doctors make informed and optimized treatment decisions, as distinguishing patients in terms of prognosis has been achieved with 94.5% accuracy. We also envisage the potential of applying our machine learning strategy to other diseases and purposes such as in designing clinical trials for unmasking the maximum benefits and minimizing risks associated with new drug candidates on given populations.
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