A Web Based Framework for Liver Disease Diagnosis using Combined Machine Learning Models

The liver is one of the most vital organs of the human body. Liver disease is any condition that harms the liver and inhibits its regular functioning. The risk presented by liver diseases is substantial, and unless it's diagnosed at an early stage, organ failure is inevitable. However, since the liver functions normally even when partially damaged, problems with patients are not discovered easily. This paper aims to present a faster solution where liver disorders can be recognised earlier by analysing the levels of enzymes in the blood. The web-based framework presented in this paper aids doctors in making quicker and more accurate diagnoses by using three optimized machine learning algorithms - weighted K-Nearest Neighbours, Decision Trees, Artificial Neural Networks- to classify the given data and produce results.