Sepsis is a major health issue causing mortality, morbidity and health care financial crisis to people around the globe. To resolve this issue, many researchers and clinical practitioners have worked hard to predict the onset of sepsis using various parameters of patients. The proposed work is an attempt of authors to analyse the various parameters (8 vital parameters, 26 laboratory or clinical parameters, and 6 demographic parameters regarding hospital stay) given in Physionet Challenge dataset so as to devise the best features for early and efficient prediction of sepsis. Authors have also addressed another important issue of missing values of some parameters of some patients by applying Gaussian Mixture Model to estimate the missing value in pre-processing steps. The pre-processed data is then fed to Extreme Gradient Boosting algorithm (XGBoost), which is a state of the art performer algorithm for prediction purposes in data analysis field. The experimental results show that by real time monitoring of data from cloud, sepsis can be predicted 6 hours prior to the onset of sepsis with an accuracy score of 0.994 and AUC score of 0.867. It is also observed that demographic parameters play a vital role in sepsis prediction. The Since the parameters used for early prediction can be easily acquired with the help of sensors, the proposed approach proves its potential for development of mobile and website applications for patient monitoring, real-time prediction of sepsis and generation of appropriate alert system.

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