Advanced Predictive Modeling for Hepatitis C Diagnosis Using Machine Learning

The hepatitis C virus (HCV) virus, which has infected nearly fifty million individuals across the world, has recently been receiving significant attention. With the advent of artificial intelligence, there is an increasing effort to predict the HCV by utilizing machine learning models and training such models on relevant datasets. These data-driven models can aid doctors in the early detection of Hepatitis C infection. In this study, using datasets from the National Center for Health Statistics, we aim to develop a reliable model for predicting patients with positive hepatitis C tests. The most significant challenge in predicting such diseases using machine learning methods is finding an appropriate technique to address extremely imbalanced datasets, which arise from the smaller number of patients compared to healthy individuals in real-world datasets. We utilized real-world datasets and employed different machine learning algorithms, along with various methods for data balancing and feature selection. Unlike previous studies, which lacked this level of comprehensiveness, we utilized diverse feature selection and sampling methods such as Recursive Feature Elimination, Analysis of Variance (ANOVA) feature selection, Correlation matrix, synthetic minority over-sampling technique (SMOTE), BorderlineSMOTE, support vector machine synthetic minority over-sampling technique (SVMSMOTE), and Adaptive Synthetic Sampling (ADASYN) in conjunction with different machine learning algorithms including Random Forest, Decision Tree, XGBoost, AdaBoost, and logistic regression. We are the first to develop such a comprehensive study for the prediction of the HCV. Our findings suggest that we can predict patients infected by the HCV with a reasonable recall score of 0.86. This recall is achieved through the application of a model using the AdaBoost algorithm and the ADASYN method for balancing the dataset. The model's performance, especially on the validation dataset, was slightly better when we used ANOVA feature selection. Feature importance analysis conducted with ANOVA feature selection and Recursive Feature Elimination indicates that four to five features are the most prominent in our research analysis to predict patients with positive Hepatitis C tests.