Intelligent Classification of Liver Diseases using Ensemble Machine Learning Techniques

Liver disease is a more challenging health crisis which affects millions of people worldwide. Early detection and treatments are essential for improving patient outcomes, but diagnosis at early stage is more challenging. Machine learning algorithms were significantly used to improve the accuracy and efficiency of liver disease diagnosis. This study developed a machine learning model to predict the stage of liver disease using a variety of clinical features. The LR Hyperparameter tuned model is used to improve the accuracy to 83% on a test set, much higher than traditional diagnostic methods. This suggests that the model could be used to develop a non-invasive, cost-effective, and highly accurate tool for diagnosing and monitoring liver disease patients. Additionally, the model could identify high-risk patients for developing liver disease complications, such as cirrhosis and liver failure. This information could inform personalized treatment plans to prevent the development of complications. Overall, the machine learning model has the potential to transform the early detection and management of liver disease.