Machine learning pipeline with an optimal feature set in the stage-wise diagnosis of hepatitis C virus

Timely and accurate diagnosis of hepatitis C Virus is aimed in the proposed research using a novel dataset. For this purpose, numerous experiments are conducted using various machine learning models employing preprocessing techniques like feature engineering and data augmentation along with multiple heterogeneous classifiers. In addition, to detecting the onset of the disease, the proposed method also detects the stage of the disease to comprehend the severity for an appropriate follow-up treatment to prevent further damage to the health of the patient. Each experiment comprises various combinations of feature engineering approaches along with multiple heterogeneous classifiers. It was found that the machine learning pipeline employing the feature engineering approach of recursive feature elimination with support vector classifier as the estimator and a stacking ensemble classifier provides the best score for all performance metrics with a F1-score of 0.95, accuracy of 95.2 and mean square error of 0.06.