@article{article_1136215, title={A Decision Support System on Artificial Intelligence Based Early Diagnosis of Sepsis}, journal={Artificial Intelligence Theory and Applications}, volume={2}, pages={14–26}, year={2022}, author={Kaya Aksoy, Pınar and Erdemir, Fatih and Kılınç, Deniz and Er, Orhan}, keywords={sepsis, early forecasting, artificial intelligence, decision support systems}, abstract={Sepsis is the intense reaction of the immune system as a result of a severe infection in any part of the body and damages to organs and tissues. And this disease is commonly fatal and costly. In this study, we perform a comparative study for Sepsis prediction using machine learning algorithms from original laboratory findings. For this purpose, thirty-two different machine learning algorithms including different tructures as well as neural network classifiers are evaluated and compared. As a result of experimental studies, SVM (Cubic, Fine Gaussian), KNN (Fine, Weighted, Subspace), Trees (Weighted, Boosted, Bagged) and neural network-based classifiers have achieved a significant success rate in the diagnosis of Sepsis using the new dataset. Thus, it is concluded that it is appropriate to use machine learning algorithms to predict whether a Sepsis patient will be survived. This study has the potential to be used as a new supportive tool for doctors when predicting Sepsis.}, number={1}, publisher={İzmir Bakırçay Üniversitesi}