使用生成对抗网络和Kolmogorov-Arnold网络进行三类分类的甲状腺疾病分类.
Aysel Topşir1, Ferdi Güler2, Ecesu Çetin3
1Department of Industrial Engineering, Yıldız Technical University, Davutpaşa, İstanbul, 34220, Türkiye.
BMC medical informatics and decision making
|August 1, 2025
概括
这项研究提高了甲状腺疾病的分类,使用生成对抗网络 (GAN) 来增强数据和Kolmogorov-Arnold网络 (KAN) 来提高准确性. 人工智能模型实现了高性能,为临床诊断提供了强大的框架.
科学领域:
- 人工智能在医学中的应用
- 机器学习用于诊断.
- 医疗数据增强 医学数据增强
背景情况:
- 准确的甲状腺疾病分类 (甲状腺功能过高,甲状腺功能低下,正常) 对于医学诊断至关重要.
- 传统的诊断方法面临着阶级不平衡和有限数据的挑战.
- 需要先进的AI技术来提高甲状腺疾病诊断的准确性和解释性.
研究的目的:
- 开发和评估用于甲状腺疾病分类的先进机器学习方法.
- 在这种情况下,研究生成对抗网络 (GAN) 对数据增强的有效性.
- 评估Kolmogorov-Arnold网络 (KAN) 的性能,并将其与传统模型进行比较.
主要方法:
- 集成生成对抗网络 (GANs) 进行合成数据生成和增强.
- 实施和评估用于甲状腺疾病分类的科尔莫戈罗夫-阿诺德网络 (KANs).
- 与传统的机器学习模型 (逻辑回归,随机森林,SVM,MLP) 进行比较,并使用SHAP/LIME进行解释性.
主要成果:
- 基于GAN的数据增强显著提高了分类准确性,特别是在少数阶级.
- 科尔莫戈罗夫-阿诺德网络 (KAN) 模型实现了98.68%的准确性,超过了传统的神经网络.
- 使用可解释性方法 (SHAP,LIME) 确定甲状腺刺激激素是最突出的预测特征.
结论:
- GAN和KAN的组合为甲状腺疾病的分类提供了一个强大而准确的框架.
- 这种人工智能驱动的方法有效地解决了阶级不平衡,并增强了模型概括能力.
- 开发的可解释的人工智能模型为临床决策支持系统提供了可行的基础,改善了患者的治疗结果.
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