A predictive study on HCV using automated machine learning models

Abstract

Hepatitis C virus (HCV) infection represents a significant contributor to chronic liver disease on a global scale. The prompt identification and management of HCV are imperative in order to avert complications and to maintain control over the disease. Nowadays, medical decision support systems that incorporate advanced diagnostic methods and effective treatment strategies are of great importance in order to make significant progress in the fight against HCV. Medical decision support systems have undergone a major evolution with the development of computer technologies. In the 2010s, the integration of big data and artificial intelligence technologies into medical decision support systems enabled rapid analysis of patient data. This has created significant synergies in the diagnostic and therapeutic approaches to various diseases. The ever-increasing volume of data on HCV infection offers opportunities to use machine learning techniques to diagnose and predict liver disorders. Although the implementation of machine learning necessitates a degree of proficiency in computer science, which frequently poses a challenge for healthcare practitioners, automated machine learning (AutoML) tools markedly mitigate this obstacle. Such tools empower users to construct highly effective machine learning models without requiring extensive technical expertise. In our investigation concerning HCV prediction, additional features were incorporated into the dataset sourced from the UCI Machine Learning Repository, and class imbalances were rectified. In our study on HCV prediction, which was conducted to address this deficiency, new features were added to the dataset obtained from the UCI Machine Learning Repository to address the deficiencies and inter-class imbalances were corrected. After this process, modeling was performed using 7 AutoML tools and high accuracy rates ranging from 99.29 % to 100 % were obtained. As an important result of this paper, these models may be regarded as a supplementary method for doctors in predicting Hepatitis C and its associated diseases.