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Hepatitis C is a global health concern that can spread through blood from infected people directly. The patient can avoid the danger of mortality by detecting this disease early. In this study, we use laboratory values and demographic information to predict Hepatitis C using 10 machine-learning algorithms such as Random Forests, Logistic Regression, Decision Trees, Support Vector Machines, Naive Bayes, K Nearest Neighbors, Gradian Boost, Boost, Artificial Neural Network and Cat Boost and 7 ensemble algorithms are GB-XGB, LR-XGB, LR-SVC, LR-KNN, LR-GB, LR-RF, SVC-XGB. Machine Learning algorithms are. This study aims to contribute to the medical sector. We get an accuracy of 99% using Logistic Regression, ANN, and SVC algorithms. Similarly, we achieve 98% accuracy using ensemble algorithms. This result demonstrates the effectiveness of our approach.

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Accurate Hepatitis C Prediction Through Rigorous Experimental Analysis Employing Ensemble Machine Learning Methods

  • 会议论文

  • 发表日期：

    2025年

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  • Md. Abdulla Hil Kafi,
  • Pritom Basak,
  • Afjal H. Sarower,
  • Subarna Akter Liza

摘要

Hepatitis C is a global health concern that can spread through blood from infected people directly. The patient can avoid the danger of mortality by detecting this disease early. In this study, we use laboratory values and demographic information to predict Hepatitis C using 10 machine-learning algorithms such as Random Forests, Logistic Regression, Decision Trees, Support Vector Machines, Naive Bayes, K Nearest Neighbors, Gradian Boost, Boost, Artificial Neural Network and Cat Boost and 7 ensemble algorithms are GB-XGB, LR-XGB, LR-SVC, LR-KNN, LR-GB, LR-RF, SVC-XGB. Machine Learning algorithms are. This study aims to contribute to the medical sector. We get an accuracy of 99% using Logistic Regression, ANN, and SVC algorithms. Similarly, we achieve 98% accuracy using ensemble algorithms. This result demonstrates the effectiveness of our approach.