Prediction of Important Factors for Bleeding in Liver Cirrhosis Disease Using Ensemble Data Mining Approach
| Autor: | Vladica Stojanović, Milan Ranđelović, Miloš Ranđelović, Aleksandar Aleksić, Mihailo Jovanović, Slobodan Nedeljković, Dragan Ranđelović, Marko Vuković, Radovan Radovanović |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Variceal bleeding
Cirrhosis classification and discrimination Calibration (statistics) General Mathematics prediction theory 02 engineering and technology Disease Logistic regression computer.software_genre ensemble techniques 03 medical and health sciences Linear regression 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) medicine Engineering (miscellaneous) 030304 developmental biology applied mathematics general theory of mathematical modeling 0303 health sciences business.industry medical applications lcsh:Mathematics data mining medicine.disease lcsh:QA1-939 Additional research linear regression 020201 artificial intelligence & image processing Data mining business Varices computer |
| Zdroj: | Mathematics Volume 8 Issue 11 Mathematics, Vol 8, Iss 1887, p 1887 (2020) |
| ISSN: | 2227-7390 |
| DOI: | 10.3390/math8111887 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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