基于多标记深度森林的膝骨关节炎智能辅助诊断方法
Intelligent assisted diagnosis method of knee osteoarthritis based on multi-label deep forest
龙锦益 1杨宇 2张子龙 2叶倩云 3吴汉瑞 2张荣华 4张佳5
作者信息
- 1. 暨南大学信息科学技术学院,广州 510632;暨南大学广东省中医药信息技术重点实验室,广州 510632;广州琶洲实验室,广州 510335
- 2. 暨南大学信息科学技术学院,广州 510632
- 3. 暨南大学广东省中医药信息技术重点实验室,广州 510632;暨南大学中医学院,广州 510632
- 4. 暨南大学广东省中医药信息技术重点实验室,广州 510632;暨南大学药学院,广州 510632;暨南大学癌症研究所,广州 510632
- 5. 暨南大学信息科学技术学院,广州 510632;暨南大学广东省中医药信息技术重点实验室,广州 510632
- 折叠
摘要
目的:利用多标记深度森林(MLDF)算法构建膝骨关节炎(KOA)智能辅助诊断模型,并探索多标记方法在中医数据集上的优势.方法:基于1 421例临床样本,使用MLDF算法构建分类模型,在6个评价指标上与其他5种多标记算法进行对比;使用多标记算法Rank-SVM、ML-kNN和单标记算法SVM、kNN建模并对比.结果:使用MLDF构建的分类模型在6个评价指标上均优于其他5种对比算法,并且在KOA标记上的AUC为0.8122,远高于其他对比算法;Rank-SVM与ML-kNN分类准确率(0.7746、0.7787)高于其对应的单标记算法(0.7641、0.7570),且在大多数评价指标上均优于其对应的单标记算法.结论:在多证兼夹的中医数据集上,多标记分类算法性能优于其对应的单标记算法,MLDF算法在KOA的诊断结果上与真实诊断结果的一致性较好,具有较好的推广和应用前景.
Abstract
Objective:To construct an intelligent assisted diagnosis model for knee osteoarthritis(KOA)using a multi-label learning with deep forest(MLDF)algorithm and explore the advantages of the multi-label method in the traditional Chinese medicine(TCM)data set.Methods:Based on 1 421 clinical samples,a classification model was constructed using a MLDF algorithm and compared with five other multi-marker algorithms on six evaluation indexes.Multi-label algorithms Rank-SVM and ML-kNN and single-label algorithms SVM and kNN were used for modeling and comparison.Results:The classification model built with the MLDF algorithm outperformed the comparison algorithms on all six evaluation metrics.The AUC for KOA was 0.8122,significantly higher than other comparison algorithms.Rank-SVM and ML-kNN showed higher classification accuracies(0.7446,0.7787)compared to their corresponding single-label algorithm(0.7641,0.7570),and they also outperformed these single-label methods in most evalution metrics.Conclusion:On the TCM data set with multiple syndromes and clips,the performance of the multi-label classification algorithm is better than that of its corresponding single-label algorithm.The diagnosis results of the MLDF algorithm in KOA are in good agreement with the confirmed diagnosis,and it has good promotion and application prospects.
关键词
膝骨关节炎/人工智能/机器学习/多标记学习/多标记深度森林/智能辅助诊断模型Key words
Knee osteoarthritis(KOA)/Artificial intelligence/Machine learning/Multi-label learning/Multi-label learning with deep forest(MLDF)/Intelligent assisted diagnosis model引用本文复制引用
出版年
2024
NETL
NSTL
万方数据