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Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease.
Hashem, Somaya; ElHefnawi, Mahmoud; Habashy, Shahira; El-Adawy, Mohamed; Esmat, Gamal; Elakel, Wafaa; Abdelazziz, Ashraf Omar; Nabeel, Mohamed Mahmoud; Abdelmaksoud, Ahmed Hosni; Elbaz, Tamer Mahmoud; Shousha, Hend Ibrahim.
Afiliação
  • Hashem S; Systems and Information Department, Engineering Research Division, National Research Centre, Giza, Egypt; Biomedical Informatics and Chemo-Informatics Group, Centre of Excellence for Medical Researches, National Research Centre, Cairo, Egypt. Electronic address: somayahashem@gmail.com.
  • ElHefnawi M; Systems and Information Department, Engineering Research Division, National Research Centre, Giza, Egypt; Biomedical Informatics and Chemo-Informatics Group, Centre of Excellence for Medical Researches, National Research Centre, Cairo, Egypt. Electronic address: mahef@aucegypt.edu.
  • Habashy S; Communications, Electronics and Computers Department, Faculty of Engineering, Helwan University, Cairo, Egypt.
  • El-Adawy M; Communications, Electronics and Computers Department, Faculty of Engineering, Helwan University, Cairo, Egypt.
  • Esmat G; Diagnostic and interventional Radiology Department, Cairo University, Cairo, Egypt.
  • Elakel W; Diagnostic and interventional Radiology Department, Cairo University, Cairo, Egypt.
  • Abdelazziz AO; Endemic Hepatogastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt.
  • Nabeel MM; Endemic Hepatogastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt.
  • Abdelmaksoud AH; Endemic Hepatogastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt.
  • Elbaz TM; Endemic Hepatogastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt.
  • Shousha HI; Endemic Hepatogastroenterology Department, Faculty of Medicine, Cairo University, Cairo, Egypt. Electronic address: hendshousha@yahoo.com.
Comput Methods Programs Biomed ; 196: 105551, 2020 Nov.
Article em En | MEDLINE | ID: mdl-32580053
ABSTRACT
BACKGROUND AND

OBJECTIVE:

Considered as one of the most recurrent types of liver malignancy, Hepatocellular Carcinoma (HCC) needs to be assessed in a non-invasive way. The objective of the current study is to develop prediction models for Chronic Hepatitis C (CHC)-related HCC using machine learning techniques.

METHODS:

A dataset, for 4423 CHC patients, was investigated to identify the significant parameters for predicting HCC presence. In this study, several machine learning techniques (Classification and regression tree, alternating decision tree, reduce pruning error tree and linear regression algorithm) were used to build HCC classification models for prediction of HCC presence.

RESULTS:

Age, alpha-fetoprotein (AFP), alkaline phosphate (ALP), albumin, and total bilirubin attributes were statistically found to be associated with HCC presence. Several HCC classification models were constructed using several machine learning algorithms. The proposed HCC classification models provide adequate area under the receiver operating characteristic curve (AUROC) and high accuracy of HCC diagnosis. AUROC ranges between 95.5% and 99%, plus overall accuracy between 93.2% and 95.6%.

CONCLUSION:

Models with simplistic factors have the power to predict the existence of HCC with outstanding performance.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Carcinoma Hepatocelular / Hepatite C Crônica / Neoplasias Hepáticas Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Comput Methods Programs Biomed Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Carcinoma Hepatocelular / Hepatite C Crônica / Neoplasias Hepáticas Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Comput Methods Programs Biomed Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article