Machine Learning-Driven Classification of Sepsis Using Influential Factor

The most prominent contemporary technique “Machine learning” performs a significant role in data analysis across various sectors. In the past few decades, these Machine learning techniques have been widely used in the medical industry for analyzing/categorizing various diseases. Various machine learning algorithms are used for analysis, especially decision tree and random forest algorithms which are one of the regression/classification techniques majorly used for analyzing various types of diseases. Sepsis is one of the life-threatening diseases that occur across various groups of people. Various attributes impact the causes/severity of sepsis. The symptom of sepsis varies based on the age group and previous health conditions. The identified sepsis dataset contains 44 attributes that include the target variable. The most common symptoms include age, rapid heart rate, length of stay in ICU, blood pressure, minerals in the body including lactate, bilirubin, blood union nitrogen, etc., white blood cells, platelets, etc. With the help of the decision tree algorithm and Random Forest, the most influential symptoms were predicted. A decision tree was constructed using entropy and Gini index where ICU length of stay was identified as the first top influential factor, followed by heart rate variation being identified as the next factor from the given dataset. Similarly, the Random Forest classifier also identifies age, and ICU length of stay as the most influencing features from the given dataset. The identified algorithm achieves a higher test accuracy of 98% using both decision trees and random forests.