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

2014 ◽  
Vol 14 (5) ◽  
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

scholarly journals Artificial Neural Network Model for Hepatitis C Stage Detection

2020 ◽  
Vol 1 (1) ◽  
pp. 11-16
Author(s):  
Dhiman Sarma ◽  
◽  
Tanni Mittra ◽  
Muntasir Hoq ◽  
Promila Haque ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chronic Hepatitis ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Infected People ◽  
Egyptian Patients ◽  
Artificial Neural ◽  
Acute And Chronic Hepatitis ◽  
Prediction Evaluation

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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