article · Knowledge and Information Systems
A machine learning framework was developed to predict Hepatitis C Virus infections among healthcare workers in Egypt. Using real-world data from 859 patients across 12 features gathered at the National Liver Institute at Menoufiya University, the investigation tested models both with and without sequential forward feature selection. Evaluated algorithms included Naïve Bayes, random forest, K-nearest neighbour, and logistic regression. Applying sequential forward feature selection produced higher classification accuracies than using unselected feature sets. The random forest classifier delivered the strongest performance, initially reaching an accuracy of 94.06 per cent with a processing time of 0.54 seconds. Following hyperparameter tuning, the random forest model improved to 94.88 per cent accuracy while requiring only four selected features.
Accurate prediction and early identification of Hepatitis C Virus can help curb disease transmission and locate infection hotspots promptly. By demonstrating that machine learning can diagnose cases with high accuracy using just four key features, this approach offers healthcare practitioners an efficient diagnostic tool that reduces data collection requirements and supports clinical decision-making.
The framework could be incorporated into clinical decision-support software for hospital specialists and healthcare programmes screening staff or high-risk populations for Hepatitis C Virus. Because the model achieves nearly 95 per cent accuracy with minimal processing time and only four input features, it represents applied research that is well suited for integration into hospital triage systems, though validation across wider healthcare settings is needed before commercial deployment.
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Abstract Prediction and classification of diseases are essential in medical science, as it attempts to immune the spread of the disease and discover the infected regions from the early stages. Machine learning (ML) approaches are commonly used for predicting and classifying diseases that are precisely utilized as an efficient tool for doctors and specialists. This paper proposes a prediction framework based on ML approaches to predict Hepatitis C Virus among healthcare workers in Egypt. We utilized real-world data from the National Liver Institute, founded at Menoufiya University (Menoufiya, Egypt). The collected dataset consists of 859 patients with 12 different features. To ensure the robustness and reliability of the proposed framework, we performed two scenarios: the first without feature selection and the second after the features are selected based on sequential forward selection (SFS). Furthermore, the feature subset selected based on the generated features from SFS is evaluated. Naïve Bayes, random forest (RF), K-nearest neighbor, and logistic regression are utilized as induction algorithms and classifiers for model evaluation. Then, the effect of parameter tuning on learning techniques is measured. The experimental results indicated that the proposed framework achieved higher accuracies after SFS selection than without feature selection. Moreover, the RF classifier achieved 94.06% accuracy with a minimum learning elapsed time of 0.54 s. Finally, after adjusting the hyperparameter values of the RF classifier, the classification accuracy is improved to 94.88% using only four features.
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DOI: 10.1007/s10115-023-01851-4
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