Intelligent diagnosis system of hepatitis C virus: A probabilistic neural network based approach

Abstract

In recent years, early detection of hepatitis C virus (HCV) disease has been a vital task in the medical science field. HCV became the main health concern to the public, as it was noticed to have more blood donors in Egypt equated to other nationalities. The WHO assessed that in 2019, around 290 000 individuals died from hepatitis C, which says the seriousness of the HCV disease. So, early prediction, preventions, and curing the disease are vital components to save individuals from HCV. In this paper, we propose and investigate experimental results of the five machine learning (ML) models and probabilistic neural network (PNN) based approach to detect the HCV utilizing University of Califonia Irvine (UCI) ML Egyptian HCV dataset. We also analyze the statistical reports of HCV stages and their features. As per result analysis, the random forest (RF) ML model performs superiorly to other traditional ML algorithms with 97.5% of accuracy. The PNN (incremental hidden layer [HL] neurons) based proposal model shows a very high performance (99.6% of accuracy) at 30-HL neurons of PNN. As per comparative analysis, the proposed model is superior to experimental basic ML models, and early HCV disease works are related to this area. This research focuses on early detection, prevention, and challenges of handling HCV.

Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques

“…Saputra et al [ 26 ] used the random forest algorithm to detect hepatitis C and achieved an accuracy of 92.5%, while Li et al [ 27 ] developed a hepatitis C virus detection model using random forest, Logistic Regression, and an ABC algorithm, and achieved a best accuracy of 94.5%. Terlapu et al [ 28 ] developed an intelligent diagnosis system for hepatitis C using a probabilistic neural network-based approach, and achieved an accuracy of 96.4%. Kaunang et al [ 29 ] compared the performance of various machine learning algorithms in predicting hepatitis C, including decision tree, random forest, naive Bayes, and SVM.…”

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