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

Overview

Journal JMIR Form Res

Publisher JMIR Publications

Specialty Biomedical Engineering

Date 2024 Dec 9

PMID 39652880

Authors

Jianchao Du

Junyao Ding

Yuan Wu

Tianyan Chen

Jianqi Lian

Lei Shi

Yun Zhou

Affiliations

Soon will be listed here.

Abstract

Background: 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.

Objective: 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.

Methods: 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.

Results: 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%.

Conclusions: 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.

References

Costa A, Scalzulli E, Carmosino I, Capriata M, Ielo C, Masucci C . Systemic mastocytosis: 2023 update on diagnosis and management in adults. Expert Opin Emerg Drugs. 2023; 28(3):153-165. DOI: 10.1080/14728214.2023.2221028. View

Wang H, Li Y, Han J, Lin Q, Zhao L, Li Q . A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer. Front Oncol. 2023; 13:1192908. PMC: 10541960. DOI: 10.3389/fonc.2023.1192908. View

Sharifrazi D, Alizadehsani R, Roshanzamir M, Joloudari J, Shoeibi A, Jafari M . Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. Biomed Signal Process Control. 2021; 68:102622. PMC: 8026268. DOI: 10.1016/j.bspc.2021.102622. View

Malekzadeh A, Zare A, Yaghoobi M, Kobravi H, Alizadehsani R . Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features. Sensors (Basel). 2021; 21(22). PMC: 8624422. DOI: 10.3390/s21227710. View

Freeman C, Kuli D, Basir O . Feature-selected tree-based classification. IEEE Trans Cybern. 2013; 43(6):1990-2004. DOI: 10.1109/TSMCB.2012.2237394. View

Balaha H, El-Gendy E, Saafan M . A complete framework for accurate recognition and prognosis of COVID-19 patients based on deep transfer learning and feature classification approach. Artif Intell Rev. 2022; 55(6):5063-5108. PMC: 8799451. DOI: 10.1007/s10462-021-10127-8. View

Wadhera T . Multimodal Kernel-based discriminant correlation analysis data-fusion approach: an automated autism spectrum disorder diagnostic system. Phys Eng Sci Med. 2023; 47(1):361-369. DOI: 10.1007/s13246-023-01350-4. View

Wang X, Wang Y, Liu D, Wang Y, Wang Z . Automated recognition of epilepsy from EEG signals using a combining space-time algorithm of CNN-LSTM. Sci Rep. 2023; 13(1):14876. PMC: 10491650. DOI: 10.1038/s41598-023-41537-z. View

Qu Y, Lin L, Shen F, Lu C, Wu Y, Xie Y . Joint Hierarchical Category Structure Learning and Large-Scale Image Classification. IEEE Trans Image Process. 2016; 26(9):4331-4346. DOI: 10.1109/TIP.2016.2615423. View

10.

Gour M, Jain S . Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification. Comput Biol Med. 2021; 140:105047. PMC: 8609674. DOI: 10.1016/j.compbiomed.2021.105047. View

11.

Sunnetci K, Alkan A . Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-ray images. Expert Syst Appl. 2022; 216:119430. PMC: 9767662. DOI: 10.1016/j.eswa.2022.119430. View

12.

Petersdorf R, Beeson P . Fever of unexplained origin: report on 100 cases. Medicine (Baltimore). 1961; 40:1-30. DOI: 10.1097/00005792-196102000-00001. View

13.

Beretta L, Santaniello A . Nearest neighbor imputation algorithms: a critical evaluation. BMC Med Inform Decis Mak. 2016; 16 Suppl 3:74. PMC: 4959387. DOI: 10.1186/s12911-016-0318-z. View

14.

Lin J, Jimenez C . Acute mediastinitis, mediastinal granuloma, and chronic fibrosing mediastinitis: A review. Semin Diagn Pathol. 2021; 39(2):113-119. DOI: 10.1053/j.semdp.2021.06.008. View

15.

Mulders-Manders C, Simon A, Bleeker-Rovers C . Fever of unknown origin. Clin Med (Lond). 2015; 15(3):280-4. PMC: 4953114. DOI: 10.7861/clinmedicine.15-3-280. View

16.

Kumar M, Varada L, Abuji K, Savlania A, Ray P, Angrup A . Mycotic aneurysm by Bacteroides fragilis presenting as fever of unknown origin. Indian J Med Microbiol. 2023; 41:53-54. DOI: 10.1016/j.ijmmb.2022.12.010. View

17.

Yuan J, Zhu W, Li H, Yan D, Shen S . Neural Network Based Classification of Breast Cancer Histopathological Image from Intraoperative Rapid Frozen Sections. J Digit Imaging. 2023; 36(4):1597-1607. PMC: 10406781. DOI: 10.1007/s10278-023-00802-3. View

18.

Ghassemi N, Shoeibi A, Khodatars M, Heras J, Rahimi A, Zare A . Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning. Appl Soft Comput. 2023; 144:110511. PMC: 10263244. DOI: 10.1016/j.asoc.2023.110511. View

19.

Okuducu Y, Nwosu A, Awad A, Basak R . Fever of Unknown Origin in a 17-Year-Old Girl. Cureus. 2020; 12(9):e10607. PMC: 7586499. DOI: 10.7759/cureus.10607. View

20.

Chen W, Zhuang J, Yu W, Wang Z . Measuring complexity using FuzzyEn, ApEn, and SampEn. Med Eng Phys. 2008; 31(1):61-8. DOI: 10.1016/j.medengphy.2008.04.005. View