| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.32 MB | Adobe PDF |
Orientador(es)
Resumo(s)
The liver is a vital organ responsible for numerous essential functions in the body. Thus, liver disorders can have severe consequences on overall health and well-being. Early diagnosis and treatment of liver disorders are crucial to prevent complications such as cirrhosis, liver failure and liver cancer. In this work, a data analysis system aims to identify the most important features in defining liver disease and categorize sick patients according to the severity of the disease. The Indian Liver Patient Dataset was evaluated using a pre-processing data analysis method that considered the Z-score, the correlation, and the Recursive Feature Elimination. After identifying the most important characteristics of the patients, the Fuzzy c-means algorithm was used to classify them based on the severity of the disease. The results of the proposed methodology proved to be effective in creating a decision support system, since it was possible to identify four levels of severity among the patients.
Descrição
Palavras-chave
Decision support Fuzzy clustering Machine learning
Contexto Educativo
Citação
Leite, Gabriel A.; Azevedo, Beatriz Flamia; Ferreira, Sofia Ribeiro; Pacheco, Maria F.; Fernandes, Florbela P.; Pereira, Ana I. (2024). Fuzzy c-Means as a Decision Support Tool for Liver Disease Diagnosis Based on Data Analysis. In 4th International Conference on Optimization, Learning Algorithms and Applications, OL2A 2024. ISSN 1865-0929. 2280, p. 97-111
Editora
Springer Nature