A Comparative Study of Machine Learning-Based Early Prediction of Sepsis

Without prompt diagnosis and treatment, sepsis is a potentially fatal condition that can cause organ failure and death. In order to improve patient outcomes, sepsis must be detected early. The ability to predict sepsis before it develops to severe stages has recently shown significant promise thanks to machine learning (ML) methods. In this study, we compare and assess the effectiveness of different machine learning (ML) models for forewarning sepsis using a publicly accessible dataset. Patients admitted to critical care units (ICUs) are included in the dataset's clinical information and diagnosed with sepsis. In order to train different ML models, such as logistic regression, decision trees, random forests, support vector machines (SVM), and artificial neural networks (ANNs), we will first preprocess the data, extract pertinent features, and then train a variety of ML models. We will assess these models' accuracy, sensitivity, specificity, and area under the curve (AUC) performance. Our results will provide insights into the most effective ML algorithm for a quick diagnosis of sepsis. This research can help clinicians make informed decisions and improve patient outcomes by predicting sepsis before it progresses to severe stages.