ensemble-based detection and classification of liver diseases caused by hepatitis c

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