Accurate Prediction of Response to Interferon-based Therapy in Egyptian Patients with Chronic Hepatitis C Using Machine-learning Approaches

ElHefnawi, Mahmoud; Abdalla, Mahmoud A.; Ahmed, Salah; Elakel, Wafaa; Esmat, Gamal; El-Raziky, Mona; Khamis, Siti Aqilah; Hassan, Mohamed F.

doi:10.1109/asonam.2012.140

Cited by 15 publications

(13 citation statements)

References 4 publications

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“…In recent years, machine-learning techniques such as classification trees and artificial neural networks (ANN) have been used as prediction, classification, and diagnosis tools [ 18 – 20 ]. Alternating decision tree (ADT) combines the simplicity of single decision tree with the effectiveness of boosting [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients

Hashem

Esmat

Elakel

et al. 2016

Gastroenterology Research and Practice

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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.

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Section: Introductionmentioning

confidence: 99%

Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients

Hashem

Esmat

Elakel

et al. 2016

Gastroenterology Research and Practice

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“…Mahmoud El Hefnawi et al [1], prediction of advanced liver fibrosis in hepatitis c patients using ANN and decision tree. ANN gives better results using the features: Albumin, AFP, Viral load, fibrosis score, and HAI and decision tree gives acceptable results from only 3 features: ALT, Fibrosis score and HAI.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey on Predicting Advanced Liver Fibrosis Using Different Machine Learning Algorithms

Krishnendu¹,

Deepa²

2020

IJSRSET

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Machine learning (ML) is a subsection of AI. The goal of ML is to understand the structure of data and fit that data into models that can be used for prediction, classification etc. Although machine learning is an area within computer science, it differs from traditional computational approaches. In recent years, different machine learning algorithms are used for disease prediction. Algorithms like Decision Tree (DT), Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Multi- Linear Regression, Random Forest, Genetic Algorithm (GA), Artificial Neural Network (ANN), Naive Bayes, etc. are used for classification. Using these algorithms liver fibrosis stages can be predicted. This paper discusses different machine learning algorithms for the prediction of liver fibrosis stage and the performance analysis of these algorithms in various studies.

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“…Hepatitis is a dangerous and transmissible disease [1][2][3][4]. The virus of this disease can spread from one infected person to another healthy human being.…”

Section: Introductionmentioning

confidence: 99%

Treatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques

et al. 2021

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The proper prognosis of treatment response is crucial in any medical therapy to reduce the effects of the disease and of the medication as well. The mortality rate due to hepatitis c virus (HCV) is high in Pakistan as well as all over the world. During the treatment of any disease, prediction of treatment response against any particular medicine is difficult. This paper focuses on predicting the treatment response of a drug: “L-ornithine L-Aspartate (LOLA)” in hepatitis c patients. We have used various machine learning techniques for the prediction of treatment response, including: “K Nearest Neighbor, kStar, Naive Bayes, Random Forest, Radial Basis Function, PART, Decision Tree, OneR, Support Vector Machine and Multi-Layer Perceptron”. Performance measures used to analyze the performance of used machine learning techniques include, “Accuracy, Recall, Precision, and F-Measure”.

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Accurate Prediction of Response to Interferon-based Therapy in Egyptian Patients with Chronic Hepatitis C Using Machine-learning Approaches

Cited by 15 publications

References 4 publications

Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients

Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients

A Survey on Predicting Advanced Liver Fibrosis Using Different Machine Learning Algorithms

Treatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques

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