Browsing by Author "Elhefnawi, Mohamed"

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Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients
(HINDAWI LTD, 2016) Hashem, Somaya; Esmat, Gamal; Elakel, Wafaa; Habashy, Shahira; Raouf, Safaa Abdel; Darweesh, Samar; Soliman, Mohamad; Elhefnawi, Mohamed; El-Adawy, Mohamed; ElHefnawi, Mahmoud
Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0-F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy.
Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients
(IEEE COMPUTER SOC., 2018) Hashem, Somaya; Esmat, Gamal; Elakel, Wafaa; Habashy, Shahira; Abdel Raouf, Safaa; Elhefnawi, Mohamed; Eladawy, Mohamed I.; ElHefnawi, Mahmoud
Background/Aim: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning techniques in prediction of advanced fibrosis by combining the serum bio-markers and clinical information to develop the classification models. Methods: A prospective cohort of 39,567 patients with chronic hepatitis C was divided into two sets-one categorized as mild to moderate fibrosis (F0-F2), and the other categorized as advanced fibrosis (F3-F4) according to METAVIR score. Decision tree, genetic algorithm, particle swarm optimization, and multi-linear regression models for advanced fibrosis risk prediction were developed. Receiver operating characteristic curve analysis was performed to evaluate the performance of the proposed models. Results: Age, platelet count, AST, and albumin were found to be statistically significant to advanced fibrosis. The machine learning algorithms under study were able to predict advanced fibrosis in patients with HCC with AUROC ranging between 0.73 and 0.76 and accuracy between 66.3 and 84.4 percent. Conclusions: Machine-learning approaches could be used as alternative methods in prediction of the risk of advanced liver fibrosis due to chronic hepatitis C.