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This research aims to enhance early prediction of sepsis using advanced machine learning and deep learning techniques.

April 19, 2026INTERNATIONAL JOURNAL OF NOVEL TRENDS AND INNOVATIONOpen Access

Sepsis Prediction System using Machine Learning and Deep Learning Approaches for Early Clinical Diagnosis

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This research aims to enhance early prediction of sepsis using advanced machine learning and deep learning techniques.
Integration of multiple predictive models, including Logistic Regression, Random Forest, XGBoost, and LSTM.
Data preprocessing techniques such as median imputation, z-score normalization, and correlation-based feature selection.
Utilization of vital signs and physiological parameters from ICU patient records for training the models.
XGBoost achieved the highest accuracy of 98.2% and AUC-ROC of 0.7114 among all models tested.
Ensemble methods demonstrated superior performance in predicting sepsis compared to individual models.
Risk stratification enables clinical decision support for personalized patient management.

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Machine learning and deep learning models predict sepsis in ICU patients, suggesting improved early diagnosis methods.

K.V.Kiran et al. (2026) studied this question.

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