Predicting of Bacteremia in Patients with Brucellosis Using Machine Learning Methods

Authors : Mehmet ÇELİK, Mehmet Reşat CEYLAN, Deniz ALTINDAĞ, Sait Can YÜCEBAŞ, Nevin GÜLER DİNCER, Sevil ALKAN
Pages : 459-468
Doi:10.16899/jcm.1243103
View : 107 | Download : 115
Publication Date : 2023-05-31
Article Type : Research Paper
Abstract :Purpose: The correct and early diagnosis of brucellosis is very crucial to decelerate its spread and providing fast treatment to patients. This study aims to develop a predictive model for diagnosing bacteremia in brucellosis patients based on some hematological and biochemical markers without the need for blood culture and bone marrow and to investigate the importance of these markers in predicting bacteremia. Materials/Methods: 162 patients with diagnosing brucellosis, 54.9% of whom are non-bacteremic, 45.1% bacteremia were retrospectively collected. The 20 demographic, hematological and biochemical laboratory parameters and 30 classifiers are used to predict bacteremia in brucellosis. Classifiers were developed by using Python programming language. Accuracy insert ignore into journalissuearticles values(ACC);, Area under the receiver operating characteristic curve insert ignore into journalissuearticles values(AROC);, and F measure were employed to find the best fit classification method. Feature importance method was used to determine most diagnostic markers to predict the bacteremia. Results: Extratree classifier with criterion “entropy” insert ignore into journalissuearticles values(ETC1); showed the best predictive performance with Acc values ranging between 0.5 and 1.00, F values between 0.53 and 1, and AROC values between 0.62 and 1. The neutrophil%, lymphocyte%, eosinophil%, alanine aminotransferase, and C-reactive protein were determined as the most distinguishing features with the scores 0.723, 1.000, 0.920, 0.869, and 0.769, respectively. Conclusions: This study showed that the ETC1 classifier may be helpful in determining bacteremia in brucellosis patients and that elevated lymphocytes, alanine aminotransferase, and C-reactive protein and low neutrophils and eosinophils may indicate bacteremic brucellosis.
Keywords : Brucellosis, Brucella, Machine learning methods, Classification, Bacteremia

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