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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.
Insights
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.
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.
Background Objectives:
The aim of this study was to identify the risk factors associated with mortality in patients diagnosed with Crimean-Congo hemorrhagic fever (CCHF) through the application of machine learning models and to evaluate their predictive performance.
Methods:
The study included patients with a definitive diagnosis who were admitted to the Department of Infectious Diseases and Clinical Microbiology of Tokat State Hospital between February 1, 2011 and October 1, 2022 with suspicion of CCHF. Five models, namely XGboost, Logistic regression, Random Forest, LightGBM, and Gradient Boosting Classifier, were constructed using machine learning algorithms to predict mortality in CCHF patients. The performance of these models was subsequently evaluated.
Results:
A total of 1,881 cases of suspected CCHF were admitted to the hospital, of which 891 were confirmed, resulting in a fatality rate of 3.3%. In the study, the receiver operating characteristic (ROC) analysis was performed to predict the risk of mortality in CCHF patients with the XGboost, logistic regression, and Gradient Boosting Classifier models. The area under the curve (AUC) results were 0.849, 0.919, and 0.853, respectively. In the evaluation of the relative importance of the features of these models, platelet count, neutrophil-to-lymphocyte ratio (NLR), and neutrophil count were identified as being among the top five.
Interpretation Conclusion:
Statistically significant predictive models were created using machine learning techniques, specifically XGboost, logistic regression, and Gradient Boosting Classifier. The results of our analysis suggest that platelet count, NLR and neutrophil count are the most effective parameters for predicting mortality.
