The performance of VCS(volume, conductivity, light scatter) parameters in distinguishing latent tuberculosis and active tuberculosis by using machine learning algorithm

Background Tuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis (MTB) and is the ninth leading cause of death worldwide. It is still difficult to distinguish active TB from latent TB,but it is very important for individualized management and treatment to distinguish whet...

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Veröffentlicht in:BMC infectious diseases Jg. 23; H. 1; S. 881 - 9
Hauptverfasser: Chen, Lijiao, Yuan, Lingke, Sun, Tingting, Liu, Ruiqing, Huang, Qing, Deng, Shaoli
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 16.12.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Accuracy
Active tuberculosis infection
Algorithms
Artificial intelligence
Classification
Computer-aided medical diagnosis
Conductivity
COVID-19
Decision trees
Diagnosis
Distinguish
Hematology
Humans
Infections
Infectious Diseases
Internal Medicine
Latent Tuberculosis - diagnosis
Latent tuberculosis infection
Learning algorithms
Leukocyte VCS (volume, conductivity, light scatter) parameters
Leukocytes (neutrophilic)
Light
Light scattering
Lymphocytes
Machine Learning
Machine learning algorithm
Mathematical models
Medical Microbiology
Medicine
Medicine & Public Health
Methods
Monocytes
Mycobacterium tuberculosis
Neutrophils
Pandemics
Parameter identification
Parasitology
Performance evaluation
Performance prediction
Regression analysis
Scattering
Support vector machines
Tropical Medicine
Tuberculosis
China
United States > US
Leukocyte VCS (volume, conductivity, light scatter) parameters
Latent tuberculosis infection
Machine learning algorithm
Active tuberculosis infection
Distinguish
ISSN:1471-2334, 1471-2334
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Zusammenfassung:Background Tuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis (MTB) and is the ninth leading cause of death worldwide. It is still difficult to distinguish active TB from latent TB,but it is very important for individualized management and treatment to distinguish whether patients are active or latent tuberculosis infection. Methods A total of 220 subjects, including active TB patients (ATB, n  = 97) and latent TB patients (LTB, n  = 113), were recruited in this study .46 features about blood routine indicators and the VCS parameters (volume, conductivity, light scatter) of neutrophils(NE), monocytes(MO), and lymphocytes(LY) were collected and was constructed classification model by four machine learning algorithms(logistic regression(LR), random forest(RF), support vector machine(SVM) and k-nearest neighbor(KNN)). And the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) to estimate of the model’s predictive performance for dentifying active and latent tuberculosis infection. Results After verification,among the four classifications, LR and RF had the best performance (AUROC = 1, AUPRC = 1), followed by SVM (AUROC = 0.967, AUPRC = 0.971), KNN (AUROC = 0.943, AUPRC = 0.959) in the training set. And LR had the best performance (AUROC = 0.977, AUPRC = 0.957), followed by SVM (AUROC = 0.962, AUPRC = 0.949), RF (AUROC = 0.903, AUPRC = 0.922),KNN(AUROC = 0.883, AUPRC = 0.901) in the testing set. Conclusions The machine learning algorithm classifier based on leukocyte VCS parameters is of great value in identifying active and latent tuberculosis infection.
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ISSN:1471-2334
1471-2334
DOI:10.1186/s12879-023-08531-2