A Multi-Metric Evaluation Framework for Enhancing Supervised Learning Models in Liver Disease Prediction

Early detection of liver disease is very important because it can help reduce the risk of liver problems. Supervised Machine Learning (ML) models help in predicting liver disease, but their performance depends on how they are evaluated and improved. In this research study, we examine different supervised learning models to understand how accurately they can predict liver disease. in this study, we use multiple evaluation methods, such as precision, recall curves, and K-Fold cross-validation to test the models more effectively, especially when the dataset is unbalanced. Several ML algorithms like Logistic Regression, Random Forest, Support Vector Machine, and Decision Tree are trained using real medical data. Many existing studies rely heavily on a single metric, such as accuracy. Our results show that using different evaluation metrics gives a better understanding of each model's performance and helps improve prediction accuracy. Overall, the study shows that using a combination of evaluation techniques can lead to more reliable Machine learning models for liver disease diagnosis