Intelligent Disease Prediction System for Hepatitis C Patients

Abstract

Accurate and timely predictions are essential for minimizing the devastating side effects of medical therapies, especially when dealing with serious illnesses like hepatitis C. In this research, a machine learning-based classification framework has been proposed to enable more reliable predictions of treatment responses in hepatitis C patients. The focus of this framework is to explore the potential of Machine Learning Techniques (MLT) in predicting, forecasting, and treating chronic diseases such as tumors, hepatitis, and heart diseases. In the proposed model, five ML algorithms - Naïve Bayes (NB), Decision Tree (DT), Bayesian Nets (BNs), Support Vector Machine (SVM), and Random Forest (RF) - are employed to predict the outcomes of hepatitis C patients undergoing treatment. The AI-enabled medical services model that has been developed is capable of delivering accurate predictions for hepatitis C with improved results in the future. Furthermore, the framework can be further enhanced by including additional ML techniques, such as deep learning or reinforcement learning, to achieve even more accurate results.