Hepatitis C Virus Detection Model by Using Random Forest, Logistic-Regression and ABC Algorithm

Abstract

This study proposes an automatic classifier for detecting the multiclass probabilities of hepatitis C virus (HCV) incidence based on patients’ blood attributes. The purpose of this study is to establish an artificial intelligence based model that can identify HCV patients and detect the disease in early stage for future treatments. This model can be applied by using clinical data and keeps the performance from imbalanced datasets. This objective was achieved using a cascade two-stage method combining the random forest (RF) and logistic regression (LR) algorithms. Two models were trained by applying the RF (Model 1) and LR (Model 2) to raw and preprocessed data, respectively. The artificial bee colony (ABC) algorithm was then used to determine the optimal threshold value required for filtering and separation, that is, the optimal combination of both models. The two-stage mixing algorithm combines algorithms of different search dimensions, thus integrating the strengths of those algorithms. The critical threshold value for separating Model 1 and Model 2 was obtained through an optimized search using the ABC algorithm. After conducting 10-fold Monte Carlo cross-validation experiments 50 times (for mean values), data from the recent pandemic were used to verify the proposed method. To evaluate the quantitative results, indicators, such as prediction accuracy, precision, recall, F1-score, and Matthews correlation coefficient, were compared with those of the latest algorithms used in relevant fields. The results indicate that the proposed model, named Cascade RF-LR (with SMOTE), can be used to detect the multiclass probabilities of HCV incidence using the ABC algorithm, thereby improving the effectiveness of relevant treatments.