| Summary: | Background: Sepsis is a life-threatening disease that occurs as a result of the body's response to an infection. This study aims to develop a classification model for predicting patients at risk of sepsis using clinical findings and demographic information. Methods: The study was conducted using a MIMICIII dataset which is freely available as open-access data. The synthetic minority oversampling technique (SMOTE) was applied to address the imbalanced data problem in our dataset. Through preprocessing, the dataset was cleaned and missing values were imputed. Split validation was done by dividing the dataset into training and test data for developing classification models. Six algorithms including Gaussian Naïve Bayes (NB), decision tree (DT), random forest (RF), logistic regression (LR), KNN algorithm, and XGBoost classifier were developed. A combination of evaluation metrics was employed to evaluate the performance of the proposed models. Results: Our dataset includes 1,552,210 entries with 44 features of critically ill patients who were admitted to the ICU. Comparing the performance of developed models using different metrics showed that the RF model had the best performance in terms of F-Measure and the area under the ROC curve. The 20 top features with high importance were determined based on the RF model. Conclusion: Our analysis showed that the RF model predicted sepsis with significantly higher performance in comparison to other classification models using the MIMICIII dataset. Due to the high mortality of sepsis, these kinds of studies could be supportive to prevent the side effects of the disease and lessen the risk of mortality in hospitalized patients by providing early sepsis prediction.
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