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%.
Recommendation systems in the online medical sector assist patients in finding appropriate doctors. This paper aimed to solve the complication in doctors' recommendations, concerning that people often struggle to see sure doctors according to their medical needs. Currently, most existing systems create doctors' recommendations through explicit or implicit feedback mechanisms. This doctor recommendation model does not depend on user feedback; instead, candidate doctors are generated for guidance solely from the user's current medical conditions. A prognosis is predicted for a specific syndrome via CNN. By applying discrete rules, the system identifies and fetches the most relevant specialists according to the prediction and provides necessary information. The performance evaluation results of the proposed method are high and satisfactory.
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