Prediction of brucellosis based on hematological biomarkers via ensemble classification methods

Authors

  • Ahmet Sahin Dr. Ersin Arslan Training and Research Hospital, Department of Infectious Diseases and Clinical Microbiology, Gaziantep, Türkiye
  • Mehmet Celik Harran University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Şanlıurfa, Türkiye
  • Mehmet Resat Ceylan Harran University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Şanlıurfa, Türkiye
  • Deniz Altındag Cizre Dr. Selahattin Cizrelioğlu State Hospital, Department of Infectious Diseases and Clinical Microbiology, Şırnak, Türkiye
  • Esra Gurbuz University of Health Sciences, Van Training and Research Hospital, Department of Infectious Diseases and Clinical Microbiology, Van, Türkiye
  • Nevin Guler Dincer Muğla Sıtkı Koçman University, Faculty of Science, Department of Statistics, Muğla, Türkiye
  • Sevil Alkan Çanakkale Onsekiz Mart University, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology, Çanakkale, Türkiye

Keywords:

Brucellosis, Ensemble classification methods, Machine learning, Permutation importance, Zoonotic disease

Abstract

Aim: Some hematological changes are frequently observed in the clinical course of brucellosis. This study aimed to predict the diagnosis of brucellosis based on some hematological biomarkers with the help of ensemble classification methods.

Materials and Methods: A total of 23 ensemble classification methods, including 10 bagging, 9 boosting, and 4 stacking approaches were applied to the brucellosis data set. Each subject in the brucellosis data set contains 13 features, including age, gender, and 10 hematological variables.

Results: This study included a total of 257 participants [173 (67.3%) brucellosis patients and 84 (32.7%) healthy controls]. The mean values of white blood cells (WBC), hemoglobin (HGB), neutrophil (NEUT), neutrophil/lymphocytes (NEUT/LYMP), and monocytes/lymphocytes (MO/LYMP) of brucellosis patients were found to be significantly lower than those of healthy controls. Random Forest with Gini criterion (RF2) was selected to be the best fit model with a mean accuracy of 0.728. HGB (mean score = 0.1814), age (0.1311), NEUT/LYMP (0.0938), WBC (0.0817) and mean platelet volume (MPV) (0.0815) were determined as most diagnostic parameters in brucellosis.

Conclusion: The lower levels of HGB, WBC, and NEUT/LYMP and higher levels of age and MPV may be important indicators for the diagnosis of brucellosis.

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Published

2023-12-27

Issue

Section

Original Articles

How to Cite

1.
Prediction of brucellosis based on hematological biomarkers via ensemble classification methods. Ann Med Res [Internet]. 2023 Dec. 27 [cited 2025 Apr. 2];30(12):1516-22. Available from: http://annalsmedres.org/index.php/aomr/article/view/4598