A Comparative Study of Machine Learning Models for Liver Disease Prediction

Liver disease diagnosis can be improved through machine learning techniques, offering early detection and better outcomes. Six machine learning models were compared like Logistic Regression, Random Forest, Gradient Boosting, MLP Classifier, SVM, and K-Nearest Neighbors (KNN) trained on clinical and demographic data. The best accuracy obtained for Logistic Regression with 89.68% accuracy, SVM of 87.30% accuracy with other models had lower accuracies of Random Forest has 85.71% and Gradient Boosting has 84.92%, while KNN is the least with an accuracy of 80.95%. These results indicate that logistic regression performs better than other algorithms.