Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning

Author:

Dou Jingrui12,Dawuti Wubulitalifu12,Zhou Jing13,Li Jintian12,Zhang Rui12,Zheng Xiangxiang4ORCID,Lin Renyong1,Lü Guodong1ORCID

Affiliation:

1. State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute The First Affiliated Hospital of Xinjiang Medical University Urumqi China

2. School of Public Health Xinjiang Medical University Urumqi China

3. College of Pharmacy Xinjiang Medical University Urumqi China

4. School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

Abstract

AbstractWhile cholecystitis is a critical public health problem, the conventional diagnostic methods for its detection are time consuming, expensive and insufficiently sensitive. This study examined the possibility of using serum fluorescence spectroscopy and machine learning for the rapid and accurate identification of patients with cholecystitis. Significant differences were observed between the fluorescence spectral intensities of the serum of cholecystitis patients (n = 74) serum and those of healthy subjects (n = 71) at 455, 480, 485, 515, 625 and 690 nm. The ratios of characteristic fluorescence spectral peak intensities were first calculated, and principal component analysis (PCA)‐linear discriminant analysis (LDA) and PCA‐support vector machine (SVM) classification models were then constructed using the ratios as variables. Compared with the PCA‐LDA model, the PCA‐SVM model displayed better diagnostic performance in differentiating cholecystitis patients from healthy subjects, with an overall accuracy of 96.55%. This exploratory study showed that serum fluorescence spectroscopy combined with the PCA‐SVM algorithm has significant potential for the development of a rapid cholecystitis screening method.

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,General Biochemistry, Genetics and Molecular Biology,General Materials Science,General Chemistry

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