HYBRID MACHINE LEARNING MODELS FOR ACCURATE LIVER DISEASE PREDICTION

Early liver disease prediction helps clinicians treat patients faster and better. Clinical datasets with demographic, biochemical, and lifestyle data are prepared using various ways. This includes outlier treatment, missing value estimate, and normalization. Selecting the right features improves model performance and noise reduction. A hybrid machine learning model uses KNN, Random Forest, Support Vector Machines, and Deep Neural Networks to make more accurate predictions. Ensemble techniques aim to reduce prediction errors and optimize classifier performance. This approach distinguishes healthy people from liver disease patients. Model effectiveness is measured by F1-score, confusion matrix, recall, accuracy, precision, and recall. Testing suggests they outperform situational models. Some patient populations and data distributions benefit from the hybrid approach. Cross-validation ensures reliable and fair performance estimates. When we consider feature value, finding crucial clinical markers that affect predictions is easy.