This paper is in the following e-collection/theme issue:

Cancer Prognosis Models and Machine Learning (67) Clinical Information and Decision Making (1915) Machine Learning (2076) Innovations and Technology in Cancer Care (531) Cancer Epidemiology, Cancer Surveillance and Infodemiology (113) Artificial Intelligence (2129)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66269, first published .
Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review

Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review

Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review

Authors of this article:

Rui Shan1 Author Orcid Image ;   Xin Li2 Author Orcid Image ;   Jing Chen1 Author Orcid Image ;   Zheng Chen3 Author Orcid Image ;   Yuan-Jia Cheng4 Author Orcid Image ;   Bo Han5, 6 Author Orcid Image ;   Run-Ze Hu1 Author Orcid Image ;   Jiu-Ping Huang7 Author Orcid Image ;   Gui-Lan Kong8, 9 Author Orcid Image ;   Hui Liu10 Author Orcid Image ;   Fang Mei11, 12 Author Orcid Image ;   Shi-Bing Song2 Author Orcid Image ;   Bang-Kai Sun13 Author Orcid Image ;   Hui Tian3 Author Orcid Image ;   Yang Wang14 Author Orcid Image ;   Wu-Cai Xiao1 Author Orcid Image ;   Xiang-Yun Yao7 Author Orcid Image ;   Jing-Ming Ye4 Author Orcid Image ;   Bo Yu7 Author Orcid Image ;   Chun-Hui Yuan2 Author Orcid Image ;   Fan Zhang7 Author Orcid Image ;   Zheng Liu1 Author Orcid Image
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Journals

  1. Li X, Yang W, Zhang F, Shan R, Mei F, Song S, Sun B, Chen J, Hu R, Yang Y, Yang Y, Liu J, Yuan C, Liu Z. Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis. JMIR Cancer 2025;11:e73069 View
  2. Aksenova A, Zhuk A, Stepchenkova E, Semenikhin V, Langovoy M. A new era of bioinformatics. Ecological genetics 2025;23(2):211 View
  3. Xu J, Xia J, Liu Y, Jiang Z, Zhao S, Zhu Y. Development and comparative evaluation of machine learning models for predicting lower extremity deep vein thrombosis in gastrointestinal cancer patients using multicenter longitudinal clinical data. Frontiers in Surgery 2025;12 View
  4. Triayudi A, Hisni D, Sari R. An adaptive multi-source fuzzy Co-clustering and knowledge distillation framework for predicting thyroid cancer recurrence: Toward interpretable and clinically deployable models. Informatics in Medicine Unlocked 2025;59:101715 View
  5. Cheng J, Chen R, Pan H, Lu L, Zhang M, He X, Yi H, Tang S. Construction and Validation of a Predictive Model for the Risk of Anti‐Tuberculosis Drug‐Induced Liver Injury Based on Machine Learning Algorithms. The Journal of Clinical Pharmacology 2025 View

Conference Proceedings

  1. Padmakala S. 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI). Comparative Analysis of Machine Learning Models for Thyroid Disease Detection: Bridging Accuracy, Interpretability, and Clinical Integration View