Application of Machine Learning and its use in determining Sepsis Prediction

Abstract

Sepsis is a potentially life-threatening and serious condition that occurs when an infection spreads across the body and triggers a widespread inflammatory response. It has a significantly high death rate, especially for patients in the ICU. Early detection and treatment of sepsis is necessary. Machine learning models for sepsis detection can be trained on a variety of data sources, such as electronic health records (EHRs), vital signs, laboratory test results, and demographic information. Exploratory Data analysis was performed using different pre-processing techniques. To classify the disease, the Random Forest Classifier is used, and comparison also performed among various Classifiers like MLP, KNN, etc. Key Words: Sepsis Prediction, Random Forest Classifier, vital signs.