Authors - Teena Bambal, Dipesh Chavan, Nikhil Gadiwadd, Deepak M. Shinde Abstract - This survey paper investigates the application of artificial intelligence (AI) and machine learning (ML) techniques for the early detection and diagnosis of liver disease. Traditional methods of liver disease diagnosis, such as blood tests and imaging techniques, can be time-consuming and prone to human error. AI-based approaches offer the potential to improve accuracy, efficiency, and accessibility of liver disease diagnosis. The research investigates a range of AI and ML algorithms, such as decision trees, support vector machines, random forests, neural networks, and deep learning models. These algorithms are applied to analyze large datasets containing patient information and medical test results. The performance of the models is evaluated using metrics such as F1-score, precision, accuracy, recall, and AUC. The findings demonstrate the effectiveness of AI-based approaches in accurately detecting liver disease. Compared to traditional methods, AI models can provide more reliable and timely diagnoses, leading to improved patient outcomes. The research highlights the potential of AI to revolutionize the field of liver disease management and improve global healthcare.
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