A predictive study on HCV using automated machine learning models
Serbun Ufuk Değer1, Hakan Can1
1Kastamonu Vocational School, Kastamonu University, 37150, Kastamonu, Turkey.
Insights
Automated machine learning (AutoML) tools can accurately predict Hepatitis C virus (HCV) infection and related liver diseases. These AI-driven models offer a valuable supplementary method for healthcare professionals in disease prediction.
Area of Science:
- Hepatology
- Medical Informatics
- Artificial Intelligence
Background:
- Hepatitis C virus (HCV) infection is a major global cause of chronic liver disease.
- Early identification and management of HCV are crucial for preventing complications and disease control.
- Advancements in medical decision support systems, particularly AI and big data, are transforming disease diagnosis and treatment.
Purpose of the Study:
- To investigate the efficacy of automated machine learning (AutoML) tools in predicting Hepatitis C virus (HCV) infection.
- To address challenges in machine learning implementation for healthcare practitioners by utilizing AutoML.
- To enhance existing datasets with additional features and correct class imbalances for improved prediction accuracy.
Main Methods:
- Utilized a dataset from the UCI Machine Learning Repository for HCV prediction.
- Incorporated additional features and rectified class imbalances within the dataset.
- Applied 7 different AutoML tools to build predictive models for HCV infection.
Main Results:
- Achieved high accuracy rates, ranging from 99.29% to 100%, using AutoML tools.
- Demonstrated the effectiveness of AutoML in overcoming technical barriers for healthcare professionals.
- Successfully addressed data deficiencies and class imbalances in the HCV dataset.
Conclusions:
- AutoML tools provide a powerful and accessible method for predicting Hepatitis C and associated liver diseases.
- These AI-driven models can serve as a valuable supplementary tool for clinicians in disease prediction.
- The study highlights the potential of AI in improving diagnostic and therapeutic approaches for HCV infection.
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.
