基于多路径特征选择的发热待查分层分类辅助诊断方法

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 Lx 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 finegrained 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.