A NOVEL METHOD FOR DETECTION OF LIVER DISEASE USING HYBRID MACHINE LEARNING ALGORITHM

Abstract

In Human beings, Liver is the most primary part of the body that performs many functions including the production of Bile, excretion of bile and bilirubin, metabolism of proteins and carbohydrates, activation of Enzymes, Storing glycogen, vitamins, and minerals, plasma proteins synthesis and clotting factors. The liver easily gets affected due to intake of alcohol, pain killer tablets, food habits, and includes plenty of wired practices. Currently, the liver related diseases are identified by analyzing liver function blood test reports and scan reports. There are several traditional methods to diagnose liver diseases, but they are expensive. Early prediction of liver disease would benefit all individuals who suffer with liver diseases for early treatment. As technology is growing in health care, machine learning significantly affects health care for predicting conditions at early stages. Therefore, by using hybrid machine learning algorithm (Support Vector Machine + Decision Tree) predicts liver disease at early stage. This method shows better results interms of accuracy, precision and detection rate.