Comprehensive Analysis to Predict Hepatic Disease by Using Machine Learning Models

Abstract

One of the tests and crucial elements in treatment planning that extends a patient’s survival is detecting the segmentation abnormalities in the liver. The death rate increases because the side effects of liver cancer cannot be recognized until the malignancy has progressed. The most excellent method to control cancer progression and preserve lives is to diagnose it early and monitor it closely. Traditional liver cancer screening methods take a long time to compute and are complex. Logistic Regression, AdaBoost, and artificial neural network approaches are utilized to predict liver disease in a person to reduce the computational procedure's complexity and improve diagnostic exactness. Artificial neural network had the highest precision, recall, and F1-score values of 0.98, 0.95, and 0.96, respectively, among the techniques discussed above. As a result, the artificial neural network can accurately identify liver illness in a person with 97.4% accuracy.