Hepatitis C Virus prediction based on machine learning framework: a real-world case study in Egypt

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.

Machine Learning–Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals

“…This research focused on exploring key clinical indices of NCDs in asymptomatic individuals. The application of machine learning in disease prediction is now well-established for its immense potential in analyzing complex datasets and uncovering patterns that may elude human detection [27][28][29][30]. The investigation employed various machine learning algorithms to predict hyperglycemia to enable early identification of individuals at a particular risk of developing diabetes.…”

Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques

“…H. Mamdouh et al [ 11 ] developed four machine learning models including naive Bayes (NB), RF, KNN, and Logistic Regression to predict hepatitis C with a dataset of 859 patients. The RF model reached the accuracy of 94.06% without and 94.88% with adjusting for the hyperparameter values of the RF classifier.…”

“…Nandipati et al [ 10 ] found that binary class labels had better performance than multiclass labels in their study, and achieved an accuracy of 54.56% using the RF model. Mamdouh et al [ 11 ] developed four machine learning models and found that the RF model had an accuracy of 94.06% without hyperparameter tuning and 94.88% with tuning. El-Salam et al [ 12 ] used multiple-classifier models and achieved accuracy rates ranging from 65.6% to 68.9%.…”

Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital

“…Te paper in [5] introduced a machine-learning framework for predicting the hepatitis C virus in Egyptian healthcare workers, showcasing improved accuracies after sequential forward selection (SFS). After hyperparameter tuning with only four features, the random forest (RF) classifer achieved 94.88% accuracy.…”