A Novel Machine Learning Approach to predict Sepsis at an Early Stage

This paper focuses on predicting the sepsis disease at an early stage using machine learning algorithms. Sepsis is a potentially serious form that occurs as a response to body's response to infection. When the response for the body's reaction goes wrong, it might cause tissue damages, organ failure or can even cause death. Because sepsis develops quickly and can be mistaken for other illnesses, it can be difficult to diagnose. Early detection of sepsis can save lives; late detection can be fatal, and it uses up a lot of hospital resources. So, in this work, an identification of sepsis patients is carried out constructed on machine learning algorithms to provide better performance. The work is developed in VISUAL STUDIO to access dynamically by using Python. The goal is to predict sepsis at an early stage using physiological data. To identify the patient's datasets and validate using algorithm with different models such as Extreme Gradient Boost, Logistic Regression, Support vector machine and the Decision tree classifiers to predict the performance metrics. In high-dimensional spaces, SVM and Logistic Regression is more effective. Boosting algorithm gives machine learning models more power to increase their prediction accuracy. Boosting algorithms are one of the most commonly utilized types of algorithms. From this Logistic Regression and Support Vector Machine provides higher accuracy of about 98%.