چکیده مقاله
Hepatitis C, induced by the hepatitis C virus HCV , represents a major public health concern due to its potential to lead to severe liver complications like fibrosis, cirrhosis, and liver cancer Without a vaccine for chronic Hepatitis C, early diagnosis and prompt treatment are crucial Traditional diagnostic methods are often time consuming, costly, and prone to false negatives, especially in early infection stages This study addresses these issues by introducing a machine learning based multi class classification framework to predict Hepatitis C severity Using laboratory data from blood donors and patients, the study employed KNN imputation, the Adaptive Synthetic Sampling ADASYN , and min max normalization for data preparation Ensemble learning methods, including voting, bagging, and boosting, were used for classification, with Bayesian optimization and K Fold cross validation for model validation According to the findings Random Forest model achieved 99% accuracy, highlighting 'Aspartic Amino Transferase' AST and 'Bilirubin' BIL as key predictors in the prediction of hepatitis C severity These methods enhance the reliability of Hepatitis C severity prediction and offering a robust tool for early diagnosis
کلیدواژهها
نویسندگان
Reza Shirazi Zadeh
Department of Industrial Engineering Yazd University Yazd, Iran
Meysam Ghanbari Marvast
Department of Industrial Engineering Yazd University Yazd, Iran
شیوه ارجاع
Shirazi Zadeh, Reza and Ghanbari Marvast, Meysam,1403,A Machine Learning-Based Framework for Multi-Class Prediction of Hepatitis C Severity Using Ensemble Techniques,The 10th International Conference on Industrial and Systems Engineering,Mashhad
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