Predicting of Bacteremia in Patients with Brucellosis Using Machine Learning Methods

Öz

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 (ACC), Area under the receiver operating characteristic curve (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” (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.

Anahtar Kelimeler

• Brucellosis
• Brucella
• Machine learning methods
• Classification
• Bacteremia

Brusellozlu Hastalarda Bakteriyeminin Makine Öğrenmesi Yöntemleri Kullanılarak Tahmin Edilmesi

Öz

Amaç: Brusellozun doğru ve erken teşhisi, yayılımını yavaşlatmak ve hastalara hızlı tedavi sağlamak için çok önemlidir. Bu çalışmanın amacı, bruselloz hastalarında bakteriyemi tanısı için kan kültürü ve kemik iliğine kültürüne ihtiyaç duymadan bazı hematolojik ve biyokimyasal belirteçlere dayalı bir prediktif model geliştirmek ve bu belirteçlerin bakteriyemiyi öngörmedeki önemini araştırmaktır. Gereç/Yöntem: Bruselloz tanısı alan %54.9'u bakteriyemik olmayan, %45.1'i bakteriyemi olan 162 hasta retrospektif olarak toplandı. Brusellozda bakteriyemiyi öngörmek için 20 demografik, hematolojik ve biyokimyasal laboratuvar parametresi ve 30 sınıflandırıcı kullanılmıştır. Sınıflandırıcılar Python programlama dili kullanılarak geliştirilmiştir. En uygun sınıflandırma yöntemini bulmak için Doğruluk (ACC), Alıcı işletim karakteristik eğrisi altındaki alan (AROC) ve F ölçüsü kullanılmıştır. Bakteriyemiyi tahmin etmek için en tanısal belirteçleri belirlemek için özellik önemi yöntemi kullanılmıştır. Sonuçlar: "Entropi" ölçütlü ekstratree sınıflandırıcı (ETC1), 0,5 ile 1,00 arasında değişen Acc değerleri, 0,53 ile 1 arasında değişen F değerleri ve 0,62 ile 1 arasında değişen AROC değerleri ile en iyi tahmin performansını gösterdi. Nötrofil %, lenfosit %, eozinofil %, alanin aminotransferaz ve C-reaktif protein sırasıyla 0,723, 1,000, 0,920, 0,869 ve 0,769 skorlarıyla en ayırt edici özellikler olarak belirlenmiştir. Sonuçlar: Bu çalışma, ETC1 sınıflandırıcısının bruselloz hastalarında bakteriyemiyi belirlemede yardımcı olabileceğini, lenfosit, alanin aminotransferaz ve C-reaktif protein yüksekliğinin; nötrofil ve eozinofil düşüklüğünün bakteremik brusellozu gösterebileceğini göstermiştir.

Anahtar Kelimeler

• Brucellosis
• Brucella
• Machine learning methods
• Classification
• Bacteremia

Kaynakça

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