Kaya Aksoy, PınarErdemir, FatihKılınç, DenizEr, Orhan2025-03-212025-03-2120222757-9778https://hdl.handle.net/20.500.14034/2776https://dergipark.org.tr/tr/pub/aita/issue/70443/1136215Sepsis 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.eninfo:eu-repo/semantics/openAccesssepsisearly forecastingartificial intelligencedecision support systemsArticle211426