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Machine learning-based mortality prediction models for Crimean-Congo hemorrhagic fever patients.

Bahadır Orkun Ozbay¹, Aliye Bastug², Arzu Ceren Yiğit³

¹Ministry of Health, Tokat State Hospital, Department of Infectious Diseases and Clinical Microbiology, Tokat, Turkey.

Journal of Vector Borne Diseases

|February 18, 2026

Summary

Machine learning models identified key risk factors for mortality in Crimean-Congo hemorrhagic fever (CCHF). Platelet count, neutrophil-to-lymphocyte ratio (NLR), and neutrophil count are crucial for predicting patient outcomes.

Keywords:

Crimean-Congo hemorrhagic fever machine learning mortality predictors

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Area of Science:

Infectious Diseases
Medical Informatics
Biostatistics

Background:

Crimean-Congo hemorrhagic fever (CCHF) poses a significant public health threat.
Identifying mortality risk factors in CCHF patients is crucial for timely intervention.
Machine learning offers advanced tools for analyzing complex disease data.

Purpose of the Study:

To identify risk factors for mortality in CCHF patients.
To evaluate the predictive performance of machine learning models for CCHF mortality.
To determine the most effective parameters for predicting CCHF-related deaths.

Main Methods:

Utilized machine learning algorithms including XGboost, Logistic Regression, Random Forest, LightGBM, and Gradient Boosting Classifier.
Developed predictive models using data from 891 confirmed CCHF cases.
Evaluated model performance using Receiver Operating Characteristic (ROC) analysis and Area Under the Curve (AUC).

Main Results:

Achieved high predictive performance with AUC values of 0.849 (XGboost), 0.919 (Logistic Regression), and 0.853 (Gradient Boosting Classifier).
Identified platelet count, neutrophil-to-lymphocyte ratio (NLR), and neutrophil count as top predictors of mortality.
The overall fatality rate among confirmed CCHF cases was 3.3%.

Conclusions:

Machine learning models, particularly Logistic Regression, XGboost, and Gradient Boosting Classifier, can effectively predict CCHF mortality.
Platelet count, NLR, and neutrophil count are significant indicators for assessing mortality risk in CCHF patients.
These findings can aid in clinical decision-making and resource allocation for CCHF management.