A decision support system on artificial intelligence based early diagnosis of sepsis
| dc.contributor.author | Kaya Aksoy, Pınar | |
| dc.contributor.author | Erdemir, Fatih | |
| dc.contributor.author | Kılınç, Deniz | |
| dc.contributor.author | Er, Orhan | |
| dc.date.accessioned | 2025-03-21T07:38:23Z | |
| dc.date.available | 2025-03-21T07:38:23Z | |
| dc.date.issued | 2022 | |
| dc.department | İzmir Bakırçay Üniversitesi | |
| dc.description.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. | |
| dc.description.sponsorship | İzmir Bakırçay Üniversitesi | |
| dc.identifier.endpage | 26 | |
| dc.identifier.issn | 2757-9778 | |
| dc.identifier.issue | 1 | |
| dc.identifier.startpage | 14 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14034/2776 | |
| dc.identifier.uri | https://dergipark.org.tr/tr/pub/aita/issue/70443/1136215 | |
| dc.identifier.volume | 2 | |
| dc.language.iso | en | |
| dc.relation.ispartof | Artificial Intelligence Theory and Applications | |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_DergiPark_20250319 | |
| dc.subject | sepsis | |
| dc.subject | early forecasting | |
| dc.subject | artificial intelligence | |
| dc.subject | decision support systems | |
| dc.title | A decision support system on artificial intelligence based early diagnosis of sepsis | |
| dc.type | Article |
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