Sepsis is a life-threatening condition characterized by an exaggerated response to infection, often leading to organ failure and death. Early detection of sepsis is crucial for improving patient outcomes, but its diagnosis remains challenging due to the complexity and subtlety of early symptoms. This project proposes a novel predictive model that combines Quantum Support Vector Classification (QSVC) with traditional machine learning techniques, including Random Forest, to enhance the early detection of sepsis. The proposed approach leverages the strengths of quantum computing for feature mapping and classification while integrating the effectiveness of machine learning models for sepsis prediction. Using a dataset from Kaggle, which includes patient files, the system predicts whether sepsis is positive or negative based on clinical features such as vital signs and laboratory data. The results aim to provide an accurate and efficient tool for early sepsis diagnosis, enabling timely intervention and improving patient survival rates.

Keywords: Sepsis, Early Detection, Quantum Support Vector Classification (QSVC), Variational Machine Learning, Random Forest, Predictive Modeling, Healthcare, Sepsis Prediction.