Liver Disease Prediction Using a Hybrid Machine Learning Approach

Liver disease poses a severe threat to human health if not detected early. Existing diagnostic methods are usually time-consuming, expensive, and require expertise, which is often unavailable in healthcare facilities. This study introduces a hybrid AI-based diagnostic framework that integrates both Deep Learning (DL) and Machine Learning (ML) techniques to support the early and accurate detection of liver disease. The proposed hybrid model integrates a MultiLayer Perceptron Neural Network (MLPNN) with a soft Voting Classifier, which includes Extreme Gradient Boosting (XGB) and Light Gradient Boosting Machine (LGBM). To enhance the predictive performance of the model, advanced feature engineering techniques were employed, including formulating medically pertinent ratios and balancing the data using SMOTE-Tomek resampling. The proposed hybrid model achieved an accuracy of 95.49%, demonstrating remarkable generalization capabilities across the dataset. The proposed model is strong and reliable, as demonstrated by the confusion matrix, classification report, and ROC-AUC curve results.