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Yousefpour, Hannah, Ghasemi, Jamal. (1402). Ensemble-Based Detection and Classification of Liver Diseases Caused by Hepatitis C. پژوهشنامه مدیریت اجرایی, 1(1), 32-42. doi: 10.22080/cste.2024.5012
Hannah Yousefpour; Jamal Ghasemi. "Ensemble-Based Detection and Classification of Liver Diseases Caused by Hepatitis C". پژوهشنامه مدیریت اجرایی, 1, 1, 1402, 32-42. doi: 10.22080/cste.2024.5012
Yousefpour, Hannah, Ghasemi, Jamal. (1402). 'Ensemble-Based Detection and Classification of Liver Diseases Caused by Hepatitis C', پژوهشنامه مدیریت اجرایی, 1(1), pp. 32-42. doi: 10.22080/cste.2024.5012
Yousefpour, Hannah, Ghasemi, Jamal. Ensemble-Based Detection and Classification of Liver Diseases Caused by Hepatitis C. پژوهشنامه مدیریت اجرایی, 1402; 1(1): 32-42. doi: 10.22080/cste.2024.5012

Ensemble-Based Detection and Classification of Liver Diseases Caused by Hepatitis C

Contributions of Science and Technology for Engineering
دوره 1، شماره 1، خرداد 2024، صفحه 32-42    اصل مقاله (1.29 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/cste.2024.5012
نویسندگان
Hannah Yousefpour؛ Jamal Ghasemi*
Faculty of Engineering & Technology, University of Mazandaran, Babolsar, Iran
تاریخ دریافت: 11 دی 1402،  تاریخ بازنگری: 13 بهمن 1402،  تاریخ پذیرش: 25 شهریور 1403 
چکیده
The liver, as the largest internal organ in the human body, plays a pivotal role in numerous physiological processes, orchestrating over 500 metabolic activities crucial for maintaining bodily functions. However, the Hepatitis C Virus (HCV) poses a grave threat to liver health, necessitating early identification of liver diseases to halt the progression to carcinoma and potentially save lives. This research aims to train ensemble-based algorithms for classifying and detecting Hepatitis, Fibrosis, and Cirrhosis. Employing rigorous preprocessing techniques, 80% of the dataset was allocated to train five ensemble-based algorithms: AdaBoost, Random Forest, Rotation Forest, XGBoost, and LightGBM. These algorithms were evaluated across four performance metrics—accuracy, precision, recall, and F1-score. Remarkably, LightGBM emerged as the frontrunner, boasting an exceptional accuracy rate of 98.37%. Rotation Forest followed closely with an accuracy of 96.74%, while XGBoost attained an accuracy of 95.12%. Random Forest and AdaBoost secured 94.19% and 93.30% accuracy, respectively. These findings underscore LightGBM’s prowess as a promising algorithm for detecting and classifying liver diseases. By leveraging advanced machine learning techniques, particularly ensemble-based algorithms, this research contributes to the ongoing efforts to enhance early detection, improve patient outcomes, and foster more effective management strategies for liver-related ailments in clinical settings
کلیدواژه‌ها
Liver Diseases؛ Machine Learning Algorithms؛ LightGBM؛ AdaBoost؛ Random Forest؛ XGBoost
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