An Auxiliary Diagnosis Method for Hierarchical Classification of FUO Based on Multi-Pathand Feature Selection
Du Jianchao1,2, Wang Yanning1, Shi Lei3, Chen Tianyan3, Liang Jingchen3, Wang Xin3, Lian Jianqi4, Zhou Yun4*
1(School of Telecommunications Engineering, Xidian University, Xi'an 710071, China) 2(Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China) 3(The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China) 4(The Second Affiliated Hospital of Air Force Medical University, Xi'an 710038, China)
Abstract：Many causes of fever of unknown origin (FUO) and high characteristic dimensions lead to difficulty in accurate diagnosis. This paper proposed an auxiliary diagnostic method based on hierarchical classification with multi-path and feature selection. Firstly, according to the structure of FUO causes, this method designed a top-down hierarchical classification model to select a controllable number of candidate categories in each middle layer, constructing a multi-path prediction mode, and finally selecting the optimal classification among multiple paths; secondly, an L1,2 paradigm regularization constraint was utilized to eliminate redundant features and preserve the optimal subset of features to reduce interference and improve prediction accuracy. In addition, this paper collected data from the First Affiliated Hospital of Xi'an Jiaotong University regarding patients visiting for FUO from 2011 to 2020 to construct a comprehensive dataset. This dataset included 564 samples and 327 dimensional features, categorized into five coarse-grained categories: bacterial infections, viral infections, other infectious diseases, autoimmune diseases, and other non-infectious diseases, and into 16 subordinate fine-grained categories. The sixteen-classification verification results on the dataset showed that when the proposed method selected 25% of the features with 3 candidate classes in the middle layer, the accuracy, FH and FLCA reached 76.08%, 86.72 % and 85.39 %, respectively, which were 9.42%, 4.69%, and 3.36% higher than the traditional single-path and non-feature selection methods, respectively. The proposed method significantly improved evaluation performance compared to the flat classification algorithms and other existing hierarchical classification algorithms, providing a more effective auxiliary diagnostic method for FUO.
杜建超, 王燕宁, 石磊, 陈天艳, 梁婧晨, 王鑫, 连建奇, 周云. 基于多路径特征选择的发热待查分层分类辅助诊断方法[J]. 中国生物医学工程学报, 2024, 43(6): 682-692.
Du Jianchao, Wang Yanning, Shi Lei, Chen Tianyan, Liang Jingchen, Wang Xin, Lian Jianqi, Zhou Yun. An Auxiliary Diagnosis Method for Hierarchical Classification of FUO Based on Multi-Pathand Feature Selection. Chinese Journal of Biomedical Engineering, 2024, 43(6): 682-692.
[1] 张文宏,李太生.发热待查诊治专家共识[J].上海医学, 2018, 41(7):385-400. [2] Choudhury A. Identification of cancer--mesothelioma disease using logistic regressio and association rule [EB/OL]. https://arxiv.org/abs/1812.10384 , 2019-08-21/2022-10-12. [3] Al-Tashi Q, Rais H, Jadid S. Feature selection method based on grey wolf optimization for coronary artery disease classification[C]//International Conference of Reliable Information and Communication Technology. Berlin: Springer, 2018: 257-266. [4] Pawlovsky AP. An ensemble based on distances for a KNN method for heart disease diagnosis[C]//2018 International Conference on Electronics, Information, and Communication (ICEIC). Piscataway: IEEE, 2018: 1-4. [5] Ogunleye A, Wang Qingguo. Xgboost model for chronic kidney disease diagnosis[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 17(6): 2131-2140. [6] Polat K. A hybrid approach to Parkinson disease classification using speech signal: the combination of smote and random forests[C]//2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT). Piscataway: IEEE, 2019: 1-3. [7] Yan Yongjie, Chen Chongyuan, Liu Yunyu, et al. Application of machine learning for the prediction of etiological types of classic fever of unknown origin[J]. Frontiers in Public Health, 2021, 9: 1-11. [8] Jiang Huizhen, Li Yuanjie, Zeng Xuejun, et al. Exploring fever of unknown origin intelligent diagnosis based on clinical data: model development and validation[J]. JMIR Med Inform. 2020,8 (11): e24375. [9] Wang Zhixiao, Liu Jian, Tian Yu, et al. Integrating medical domain knowledge for early diagnosis of fever of unknown origin: an interpretable hierarchical multimodal neural network approach[J]. IEEE Journal of Biomedical and Health Informatics, 2023,27(11): 5237-5248. [10] 陈景年.一种适于多分类问题的支持向量机加速方法[J].计算机科学,2022,49(S1):297-300. [11] 胡清华, 王煜, 周玉灿,等. 大规模分类任务的分层学习方法综述[J]. 中国科学: 信息科学, 2018, 48(5): 487-500. [12] Freeman C, Kulic D, Basir O. Feature-selected tree-based classification[J]. IEEE Transactions on Cybernetics, 2013, 43(6): 1990-2004. [13] 周平, 吴忠卫, 张瑞垚, 等. 基于稀疏表示剪枝集成建模的烧结终点位置智能预测[J]. 控制理论与应用, 2024,41(3):436-446. [14] Zhou Quan, Song Shiji, Wu Cheng, et al. Kernelized lars–lasso for constructing radial basis function neural networks[J]. Neural Computing and Applications, 2013, 23(7): 1969-1976. [15] Xu Zongben, Guo Hailiang, Wang Yao, et al. Representative of L1,2regularization among Lq (0< q 1) regularizations: an experimental study based on phase diagram[J]. Acta Autom Sin, 2012, 38(7): 1225-1228. [16] Wang X, Li Y, Wang D. The PSO and the taylor expansion based LSSVM algorithm for EEG fatigue multi-classification[C]//2023 IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT). Piscataway: IEEE, 2023: 1070-1074. [17] Galetto FJ, Deng G. Reverse image filtering using total derivative approximation and accelerated gradient descent[J]. IEEE Access, 2022, 10: 124928-124944. [18] Liu Jun, Ji Shuiwang, Ye Jieping. SLEP: sparse learning with efficient projections [EB/OL]. http://yelabs.net/software/SLEP/manual.pdf , 2011-12-28/2022-10-12. [19] Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems [J]. SIAM Journal on Imaging Sciences, 2009, 2 (1): 183-202. [20] Gomez JC, Moens MF. Hierarchical classification of web documents by stratified discriminant analysis[C]//Multidisciplinary Information Retrieval: 5th Information Retrieval Facility Conference. Vienna: Springer Berlin Heidelberg, 2012: 94-108. [21] Schieber B, Vishkin U. On finding lowest common ancestors: Simplification and parallelization[J]. SIAM Journal on Computing, 1988, 17(6): 1253-1262. [22] Dekel O, Keshet J, Singer Y. Large margin hierarchical classification[C]//Proceedings of the21st International Conference on Machine Learning. Alberta: Association for Computing Machinery, 2004: 27-34. [23] Liu L. Research on logistic regression algorithm of breast cancer diagnose data by machine learning[C]//2018 International Conference on Robots & Intelligent System (ICRIS). Piscataway: IEEE, 2018: 157-160. [24] Qu Yanyun, Lin Li, Shen Fumin, et al. Joint hierarchical category structure learning and large-scale image classification[J]. IEEE Transactions on Image Processing, 2016, 26(9): 4331-4346. [25] Zheng Weijie, Zhao Hong. Cost-sensitive hierarchical classification for imbalance classes[J]. Applied Intelligence, 2020, 50(8): 2328-2338. [26] Zheng Weijie, Zhao Hong. Cost-sensitive hierarchical classification via multi-scale information entropy for data with an imbalanced distribution[J]. Applied Intelligence, 2021, 51: 5940-5952.
