ArticleCancers2024
Explainable Thyroid Cancer Diagnosis Through Two-Level Machine Learning Optimization with an Improved Naked Mole-Rat Algorithm.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- 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
- Capsule Neural Networks with Bayesian Optimization for Pediatric Pneumonia Detection from Chest X-Ray Images.Journal of clinical medicine · 2025Article
- Diagnostics of diabetic retinopathy based on fundus photos using machine learning methods with advanced feature engineering algorithms.Scientific reports · 2025Article
- Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction.BMC medical informatics and decision making · 2025Article
- Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis.Scientific reports · 2025Article
- An integrated approach of feature selection and machine learning for early detection of breast cancer.Scientific reports · 2025Article
- Towards precision oncology: a multi-level cancer classification system integrating liquid biopsy and machine learning.BioData mining · 2025Article
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1 author.
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Abstract
Modern technologies, particularly artificial intelligence methods such as machine learning, hold immense potential for supporting doctors with cancer diagnostics. This study explores the enhancement of popular machine learning methods using a bio-inspired algorithm-the naked mole-rat algorithm (NMRA)-to assess the malignancy of thyroid tumors. The study utilized a novel dataset released in 2022, containing data collected at Shengjing Hospital of China Medical University. The dataset comprises 1232 records described by 19 features. In this research, 10 well-known classifiers, including XGBoost, LightGBM, and random forest, were employed to evaluate the malignancy of thyroid tumors. A key innovation of this study is the application of the naked mole-rat algorithm for parameter optimization and feature selection within the individual classifiers. Among the models tested, the LightGBM classifier demonstrated the highest performance, achieving a classification accuracy of 81.82% and an F1-score of 86.62%, following two-level parameter optimization and feature selection using the naked mole-rat algorithm. Additionally, explainability analysis of the LightGBM model was conducted using SHAP values, providing insights into the decision-making process of the model.
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