Machine Learning in Liver Disease Detection: A Comprehensive Review
Keywords:
Artificial Neural Network (ANN), Big data, Learning algorithm, Logistic, Logistic regression, ML algorithm, ROCAbstract
Liver diseases, including Non Alcoholic Fatty Liver Disease (NAFLD) and cirrhosis, present significant global health challenges. Early detection of liver diseases is crucial for effective treatment and management. Traditional diagnostic methods such as liver biopsies and ultrasound imaging are widely used but have limitations in terms of accuracy and accessibility. Machine Learning (ML) has emerged as a promising tool for improving liver disease diagnosis by automating image analysis and identifying patterns in clinical data. This study evaluates the performance of three ML models Random Forest, Logistic Regression, and Decision Tree for classifying liver ultrasound images. Using a dataset of B mode ultrasound images, models were trained and tested, with Logistic Regression achieving the highest accuracy (82%). The study highlights the potential of ML in liver disease detection and suggests future improvements through deep learning approaches.
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