Diagnosis of different Liver Disease Phases using Machine Learning Techniques in Non- Invasive Method

Abstract

Suffering from Liver Disease is increasing day to day life due to excessive drinking alcohol. Alcohol Consumption is the major cause for the liver damage. Some of the reasons suffering for liver disease they are inhalation of the polluted gas,  edited  by drugs,  unhealthy food, pickles. The day to day life prediction of liver disease is utilized because it gives the best results and high accuracy by using Machine Learning models are trouble-free to diagnose  infection at initial stage. These techniques are new advantage for the Healthcare purposes. Early prediction of disease is very important before effecting other organs and fast recovery. The techniques of ML (Machine Learning) used to diagnose the liver disease through SVM, Decision tree, Random Forest, KNN, K-Means, Neural Network etc. By using these  models  we  can predict the results like precision, accuracy, sensitivity. The goal of this paper is comparison of machine learning models for diagnosing the liver disease stages, and prognosis of liver condition in medicative sector.

Keywords: Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Machine Learning Techniques, Non-Invasive approach, Physical layer, Serum Markers.