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Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression.

Jichong Zhu1, Weiming Tan1, Xinli Zhan1

  • 1The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, P. R. China.

BMC Immunology
|September 26, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a machine learning (ML) model to predict Human Leukocyte Antigen-B27 (HLA-B27) positivity. The model aids in diagnosing rheumatic diseases and immune conditions.

Keywords:
HLA-B27Immunological diseasesMachine learning algorithmsNomogramPrediction model

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Area of Science:

  • Immunogenetics
  • Computational Biology
  • Rheumatology

Background:

  • Human Leukocyte Antigen-B27 (HLA-B27) positivity is common in patients with rheumatic diseases.
  • HLA-B27 testing is crucial for diagnosing various conditions.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for predicting HLA-B27 positivity.
  • To identify key biomarkers for HLA-B27 status prediction.

Main Methods:

  • Screened 1503 patients undergoing HLA-B27 examination and routine tests.
  • Employed LASSO, SVM recursive feature elimination, and random forest for feature selection.
  • Constructed a diagnostic nomogram using selected predictive factors.

Main Results:

  • Six factors including red blood cell count and albumin/globulin ratio were identified.
  • The ML model achieved an AUC of 0.825 in the training set and 0.853 in the validation set.
  • A significant decrease in albumin/globulin ratio was observed in HLA-B27 positive and ankylosing spondylitis cases.

Conclusions:

  • The proposed ML model effectively predicts HLA-B27 status.
  • This tool can assist clinicians in diagnosing immune-related diseases.