Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 24th Aug 2026, 05:31:42am America, Santiago
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Daily Overview |
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23A: Computer Science Virtual location: VIRTUAL: Agora Meetings | |
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12:40pm - 12:48pm
Intelligent Models for the Detection of Liver Cirrhosis with Emphasis on Imbalanced Classes 1: Universidad de Guayaquil - (EC), Ecuador; 2: Facultad de Ciencias Matemáticas y Física; 3: Facultad de Ciencias Administrativas; 4: Facultad de Ciencias Económicas; 5: Escuela Superior Politécnica Del Litoral - ESPOL - (EC); 6: St Luke’s University Hospital Network - (US),United States(PA); 7: Grupo de Investigación de Inteligencia Artificial Liver cirrhosis is a leading cause of morbidity and mortality worldwide, with an increasing prevalence associated with multiple etiologies. Accurate prediction of survival in cirrhotic patients is crucial for risk stratification and the optimization of therapeutic resources, particularly in identifying candidates for liver transplantation. This study comparatively evaluated three machine learning approaches: Random Forest with class weights, Artificial Neural Network with SMOTE oversampling, and a Fuzzy Logic classifier with reinforced rules, using the public dataset from the Mayo Clinic Trial (n=8,181). The class distribution showed extreme imbalance: 62.5% censored, 33.9% deceased, and 3.6% transplanted. The results showed that Random Forest achieved the best overall performance (Balanced Accuracy=0.652, F1-macro=0.666), with particularly outstanding accuracy in the majority classes (F1-censored=0.866, F1-death=0.760). | |