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

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.

An Ensemble Method for Determining the Importance of Selected Social and Health Factors Affecting the Inpatient Treatment Quality

“…From other site from planed research in technique and methodology for solving considered healthcare problem and problems in healthcare which already was subject of research of authors [114,115] authors will deal in feature with other from many different problems in healthcare and other fields of human life as for example traffic and education which the authors have already done [116,117].…”

Assessing the Efficiency of Foreign Investment in a Certification Procedure Using an Ensemble Machine Learning Model

“…To sum up, various ensemble models previously published by the coauthors of this study can be found in references [114][115][116][117][118]. These models employ diverse strategies such as maximum voting or triple modular redundancy, combined with various classification and machine learning (ML) algorithms, to address a range of problems in human life.…”

Dynamics under Uncertainty: Modeling Simulation and Complexity

“…This issue contains the successful invited submissions [1][2][3][4][5][6][7][8][9][10][11] to a Special Issue of Mathematics on the subject area of "Dynamics under Uncertainty: Modeling Simulation and Complexity".…”

“…Li et al [4] investigated the problems of state feedback and the static output feedback preview controller for uncertain discrete-time multiple-input multiple-output The geographical distribution of the authors (published papers) is presented in Table 1. Published submissions are related to road traffic risk analysis [1], dual-rotor systems [2], multi-criteria decision making [3,5,6,8,9], MIMO discrete-time systems [4], the classification and diagnosis of brain disease [7], data mining [10], and empathic building [11].…”

“…For defining the objective weights of the criteria, they applied the correlation coefficient and the standard deviation, and for the final ranking of the alternatives, they utilized the MARCOS method. Aleksić et al [10] developed a prediction model that determines the most important factors for bleeding in liver cirrhosis. Salmeron and Ruiz-Celma [11] proposed an artificial intelligence-based approach to detect synthetic emotions based on Thayer's emotional model and Fuzzy Cognitive Maps.…”