From Data to Diagnosis: A Machine Learning-Enabled Framework for Early Sepsis Prediction and Prevention

The rising prevalence of chronic diseases, driven by population ageing, emerging pathogens, and evolving lifestyles, necessitates stronger healthcare systems that integrate effective prevention with timely intervention. Sepsis remains one of the most critical and life-threatening conditions, associated with high incidence, mortality, and morbidity, and frequently progressing to multiple organ dysfunction and septic shock. Early identification is therefore essential to improve patient outcomes. In this work, we propose a rapid and accurate data-driven framework for early sepsis prediction. The framework comprises four stages: data collection, preprocessing, preparation, and classification. Real-world clinical data from 1000 patients are utilized for early risk assessment. Data preprocessing focuses on cleaning and extracting clinically relevant features, followed by data preparation steps including labeling, dataset splitting, class balancing, and feature scaling. Multiple machine learning and neural network models are then implemented, with optimized parameter selection to enhance predictive performance. Finally, a deployment module enables healthcare professionals to leverage the trained models for real-time patient status assessment, supporting timely clinical decision-making. Extensive experimental results demonstrate that the proposed framework achieves fast and accurate discrimination between septic and non-septic patients, outperforming existing state-of-the-art approaches.