article · Diagnostics
Hepatitis C virus infection presents a major public health challenge in Egypt, where transmission has been linked to factors including injection drug use, poor sterilisation practices in healthcare facilities, and limited public awareness. To improve prediction of the disease, the hyOPTGB framework introduces an optimised gradient boosting classifier. The pipeline applies Min-Max normalisation for feature scaling and forward selection to identify the most critical variables from a dataset of 1,385 instances and 29 features sourced from the UCI machine learning repository. Hyperparameters were tuned using the OPTUNA framework to maximise diagnostic efficacy. When evaluated against decision trees, support vector machines, dummy classifiers, ridge classifiers, and bagging classifiers, the hyOPTGB model achieved a superior prediction accuracy of 95.3 per cent, while also surpassing alternative models previously evaluated on the same benchmark dataset.
Hepatitis C remains a critical healthcare challenge in high-prevalence settings such as Egypt. Computational models that accurately predict infection status from clinical indicators can assist medical practitioners with faster, data-driven screening. Establishing robust machine learning pipelines with high accuracy helps refine diagnostic protocols, enabling targeted interventions and better resource management in heavily burdened public health systems.
The model could form the basis of a diagnostic decision-support tool for clinicians, testing centres, or healthcare software vendors. Currently, the technology represents early-stage algorithm development tested retrospectively on a public repository dataset. Commercial deployment would require integration into electronic health record workflows, prospective clinical validation, and regulatory clearance for medical diagnostic software before it could be applied in real-world healthcare delivery.
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The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model's accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system's efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset.
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DOI: 10.3390/diagnostics13223439
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