文章：

通过改进裸鼹鼠算法的两级机器学习优化实现可解释的甲状腺癌诊断

Explainable Thyroid Cancer Diagnosis Through Two-Level Machine Learning Optimization with an Improved Naked Mole-Rat Algorithm

原文发布日期：10 December 2024

DOI: 10.3390/cancers16244128

类型: Article

开放获取: 是

英文摘要：

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.

摘要翻译： 

现代技术，特别是机器学习等人工智能方法，在辅助医生进行癌症诊断方面具有巨大潜力。本研究探索利用仿生算法——裸鼹鼠算法（NMRA）来增强主流机器学习方法，以评估甲状腺肿瘤的恶性程度。研究采用了中国医科大学附属盛京医院于2022年发布的新型数据集，该数据集包含1232条记录，由19个特征描述。本研究使用了包括XGBoost、LightGBM和随机森林在内的10种知名分类器来评估甲状腺肿瘤的恶性程度。本研究的核心创新在于将裸鼹鼠算法应用于各分类器的参数优化和特征选择。在测试的模型中，LightGBM分类器经过裸鼹鼠算法的两级参数优化和特征选择后表现出最佳性能，分类准确率达到81.82%，F1分数达到86.62%。此外，研究还利用SHAP值对LightGBM模型进行可解释性分析，从而揭示模型的决策过程。

原文链接：

Explainable Thyroid Cancer Diagnosis Through Two-Level Machine Learning Optimization with an Improved Naked Mole-Rat Algorithm