Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/80679
Título: | Predicting the survival of primary biliary cholangitis patients |
Autor(es): | Ferreira, Diana Neto, Cristiana Lopes, José Duarte, Júlio Miguel Marques Abelha, António Machado, José Manuel |
Palavras-chave: | Classification Data mining Predictive models Primary biliary cholangitis |
Data: | 11-Ago-2022 |
Editora: | Multidisciplinary Digital Publishing Institute |
Revista: | Applied Sciences |
Citação: | Ferreira, D.; Neto, C.; Lopes, J.; Duarte, J.; Abelha, A.; Machado, J. Predicting the Survival of Primary Biliary Cholangitis Patients. Appl. Sci. 2022, 12, 8043. https://doi.org/10.3390/app12168043 |
Resumo(s): | Primary Biliary Cholangitis, which is thought to be caused by a combination of genetic and environmental factors, is a slow-growing chronic autoimmune disease in which the human body’s immune system attacks healthy cells and tissues and gradually destroys the bile ducts in the liver. A reliable diagnosis of this clinical condition, followed by appropriate intervention measures, can slow the damage to the liver and prevent further complications, especially in the early stages. Hence, the focus of this study is to compare different classification Data Mining techniques, using clinical and demographic data, in an attempt to predict whether or not a Primary Biliary Cholangitis patient will survive. Data from 418 patients with Primary Biliary Cholangitis, following the Mayo Clinic’s research between 1974 and 1984, were used to predict patient survival or non-survival using the Cross Industry Standard Process for Data Mining methodology. Different classification techniques were applied during this process, more specifically, Decision Tree, Random Tree, Random Forest, and Naïve Bayes. The model with the best performance used the Random Forest classifier and Split Validation with a ratio of 0.8, yielding values greater than 93% in all evaluation metrics. With further testing, this model may provide benefits in terms of medical decision support. |
Tipo: | Artigo |
Descrição: | Data are available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analysed in this study. These data can be found here: https://www.kaggle.com/jixing475/mayo-clinic-primary-biliary-cirrhosis-data (accessed on 1 July 2022). |
URI: | https://hdl.handle.net/1822/80679 |
DOI: | 10.3390/app12168043 |
e-ISSN: | 2076-3417 |
Versão da editora: | https://www.mdpi.com/2076-3417/12/16/8043 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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applsci-12-08043.pdf | 1,24 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons