Prediction of Important Factors for Bleeding in Liver Cirrhosis Disease Using Ensemble Data Mining Approach

Aleksandar Aleksić, Slobodan Nedeljković, Mihailo Jovanović, Miloš Ranđelović, Marko Vuković, Vladica Stojanović, Radovan Radovanović, Milan Ranđelović and Dragan Ranđelović
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Aleksandar Aleksić: Faculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, 11000 Beograd, Serbia
Slobodan Nedeljković: Ministry of Interior, Government of the Republic of Serbia, 11000 Beograd, Serbia
Mihailo Jovanović: Office for Information Technologies and e-Government, Government of the Republic of Serbia, 11000 Beograd, Serbia
Miloš Ranđelović: Magna Seating D.O.O., 25250 Odžaci, Serbia
Marko Vuković: Public Utillity Company for Underground Exploatation of Coal Resavica, 11000 Beograd, Serbia
Vladica Stojanović: Department of Information Technology, University of Criminal Investigation and Police Studies, 11000 Beograd, Serbia
Radovan Radovanović: Department of Forensic Engineering, University of Criminal Investigation and Police Studies, 11000 Beograd, Serbia
Milan Ranđelović: Science Technology Park Niš, 18000 Niš, Serbia
Dragan Ranđelović: Faculty of Diplomacy and Security, University Union-Nikola Tesla Belgrade, 11000 Beograd, Serbia

Mathematics, 2020, vol. 8, issue 11, 1-22

Abstract: The main motivation to conduct the study presented in this paper was the fact that due to the development of improved solutions for prediction risk of bleeding and thus a faster and more accurate diagnosis of complications in cirrhotic patients, mortality of cirrhosis patients caused by bleeding of varices fell at the turn in the 21th century. Due to this fact, an additional research in this field is needed. The objective of this paper is to develop one prediction model that determines most important factors for bleeding in liver cirrhosis, which is useful for diagnosis and future treatment of patients. To achieve this goal, authors proposed one ensemble data mining methodology, as the most modern in the field of prediction, for integrating on one new way the two most commonly used techniques in prediction, classification with precede attribute number reduction and multiple logistic regression for calibration. Method was evaluated in the study, which analyzed the occurrence of variceal bleeding for 96 patients from the Clinical Center of Nis, Serbia, using 29 data from clinical to the color Doppler. Obtained results showed that proposed method with such big number and different types of data demonstrates better characteristics than individual technique integrated into it.

Keywords: ensemble techniques; data mining; classification and discrimination; linear regression; applied mathematics general; prediction theory; theory of mathematical modeling; medical applications (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:11:p:1887-:d:437836

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