Comparative Analysis Of Early Detection Techniques For Liver Diseases Through Python Algorithms

Abstract

Early diagnosis of liver disease is very important in order to save human lives and take appropriate measure to control the disease. In several fields, especially in the field of medical science, the ensemble method was successfully applied. This research work uses different ensemble methods to investigate the early detection of liver disease. The selected data set for this analysis is made up of attributes such as total bilirubin, direct bilirubin, age, sex, total protein, albumin, and globul in ratio. This research mainly aims at measuring and comparing the efficiency of different ensemble methods. Ada Boost, Logit Boost, BeggJ48 and Random Fore stare the ensemble method used in this research. The study shows that Logit Boost is the most accurate model than other ensemble approaches.