Background

Sepsis remains one of the leading causes of mortality worldwide, accounting for millions of deaths annually. Early recognition and intervention significantly improve patient outcomes; however, clinical diagnosis is often delayed due to the heterogeneous nature of symptoms. Recent advances in machine learning (ML) have demonstrated significant potential in analyzing complex clinical datasets and facilitating early sepsis prediction.

Objective

This study evaluates the effectiveness of various machine learning algorithms in the early detection of sepsis using electronic health record (EHR) data and compares their predictive performance.

Methods

A retrospective observational study was conducted using anonymized patient records from intensive care units. Data preprocessing included handling missing values, feature engineering, normalization, and class balancing. Multiple machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were developed and evaluated using Accuracy, Precision, Recall, F1-score, and Area Under Receiver Operating Characteristic Curve (AUC-ROC).

Results

XGBoost achieved the highest performance with an AUC-ROC of 0.94, followed by Random Forest (0.91) and ANN (0.89). Important predictive features included heart rate, respiratory rate, white blood cell count, serum lactate level, and mean arterial pressure.

Conclusion

Machine learning algorithms can substantially improve early sepsis detection compared to traditional clinical scoring systems. Integration of ML-based predictive models into clinical decision support systems may facilitate timely intervention and reduce sepsis-related mortality.