A Pathological Diagnosis Method for Fever of Unknown Origin Based on Multipath Hierarchical Classification: Model Design and Validation

• Published in: JMIR formative research 2024-12, Vol.8, p.e58423-e58423
• Main Authors: Du, Jianchao, Ding, Junyao, Wu, Yuan, Chen, Tianyan, Lian, Jianqi, Shi, Lei, Zhou, Yun
• Format: Article
• Language: English
• Subjects: Algorithms; China; Computer-Aided Diagnosis; Development and Evaluation of Research Methods, Instruments and Tools; Female; Fever of Unknown Origin - etiology; Humans; Infectious Diseases (non-STD/STI); Machine Learning; Male; Original Paper; Tools, Programs and Algorithms
• ISSN: 2561-326X; 2561-326X
• DOI: 10.2196/58423

Fever of unknown origin (FUO) is a significant challenge for the medical community due to its association with a wide range of diseases, the complexity of diagnosis, and the likelihood of misdiagnosis. Machine learning can extract valuable information from the extensive data of patient indicators, aiding doctors in diagnosing the underlying cause of FUO. The study aims to design a multipath hierarchical classification algorithm to diagnose FUO due to the hierarchical structure of the etiology of FUO. In addition, to improve the diagnostic performance of the model, a mechanism for feature selection is added to the model. The case data of patients with FUO admitted to the First Affiliated Hospital of Xi'an Jiaotong University between 2011 and 2020 in China were used as the dataset for model training and validation. The hierarchical structure tree was then characterized according to etiology. The structure included 3 layers, with the top layer representing the FUO, the middle layer dividing the FUO into 5 categories of etiology (bacterial infection, viral infection, other infection, autoimmune diseases, and other noninfection), and the last layer further refining them to 16 etiologies. Finally, ablation experiments were set to determine the optimal structure of the proposed method, and comparison experiments were to verify the diagnostic performance. According to ablation experiments, the model achieved the best performance with an accuracy of 76.08% when the number of middle paths was 3%, and 25% of the features were selected. According to comparison experiments, the proposed model outperformed the comparison methods, both from the perspective of feature selection methods and hierarchical classification methods. Specifically, brucellosis had an accuracy of 100%, and liver abscess, viral infection, and lymphoma all had an accuracy of more than 80%. In this study, a novel multipath feature selection and hierarchical classification model was designed for the diagnosis of FUO and was adequately evaluated quantitatively. Despite some limitations, this model enriches the exploration of FUO in machine learning and assists physicians in their work.