Artificial Neural Network Model for Hepatitis C Stage Detection

Hepatitis C is a liver disease caused by the hepatitis C virus (HCV). In 2015, WHO reports that 71 million people were living with HCV, and 1.34 million died. In 2017, 13.1 million infected people knew their diagnosis and around 5 million patients were treated. HCV can cause acute and chronic hepatitis, where 20% of chronic hepatitis progresses to final-stage chronic liver cancer. Currently, no vaccine of HCV exists, and no effective treatments are available for demolishing the progression of hepatitis C. So spotting the stages of the disease is essential for diagnostic and therapeutic management of infected patients. This paper attempts to detect stages of hepatitis C virus so that further diagnosis and medication of hepatitis patients can be prescribed. It uses a supervised artificial neural network to make a prediction. Evaluation of results is done by cross-validation using the holdout method. Hepatitis C Egyptian-patients' dataset from UCI Machine Learning Repository is used for feeding the algorithms. The research succeeds to detect the hepatitis C stages and achieves an accuracy of 97%.

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Predicting the Outcomes of Combination Therapy in Patients With Chronic Hepatitis C Using Artificial Neural Network

Hepatitis Monthly ◽

10.5812/hepatmon.17028 ◽

2014 ◽

Vol 14 (5) ◽

Cited By ~ 3

Author(s):

Forough Sargolzaee Aval ◽

Nazanin Behnaz ◽

Mohamad Reza Raoufy ◽

Seyed Moayed Alavian

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Chronic Hepatitis ◽

Hepatitis C ◽

Combination Therapy ◽

Chronic Hepatitis C ◽

Artificial Neural

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An improved percentage rate accuracy in predicting mortality in hepatitis-c using an artificial neural network

Journal of Computer Science and Its Application ◽

10.4314/jcsia.v27i2.2 ◽

2021 ◽

Vol 27 (2) ◽

Author(s):

Daniel Matthias ◽

I.N. Davies ◽

O. Olumide

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Hepatitis C Virus ◽

Hepatitis C ◽

Network Architecture ◽

Learning Algorithm ◽

Object Oriented Design ◽

Intelligent Technique ◽

Prediction Of Mortality ◽

Artificial Neural

Background accurate prediction of mortality in Hepatitis-C (Hep C) is essential for policy action and planning. While studies have used artificial intelligent technique (e.g., artificial neural network (ANN)), their appropriateness to predicting mortality in hepatitis-c has been debated. This study presents an improved percentage rate accuracy that is capable of predicting whether a patient suffering from Hepatitis-C Virus (HCV) is likely to survive or die. The constructive research method was adopted for this study, while an Object Oriented Design Approach was adopted for the systems structural design. The Artificial Neural Network system was implemented using java programming language with many program modules and four (4) design classes namely; the Driver class that runs the application program, the Neural Network class, the Neuron and the Layer classes. The network was trained using back propagation machine learning algorithm, a learning rate of 0.8 and a learning error of 0.05. While the weights used for the training were random numbers ranging from -1.0 to +1.0. The maximum number of training sessions was set to 10000 assuming the network does not converge to the leaning error of 0.05. The result of the network showed 85% accuracy in predicting cases of the patients with positive hepatitis C virus that may survive and also 50% accuracy in predicting cases of patients with positive Hepatitis-C Virus (HCV) that may likely to die given the provided data. Neural network is a powerful classification and prediction tools that can help in predicting the outcome of Hepatitis-C virus (HCV) infections. Recommending experiment on the network architecture with a view to either increase the hidden layers or increasing the number of units in the hidden layer. Also, more extensive testing and training should be carried out to achieve the desired result.

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