期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2018
卷号：8
期号：2
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Early prediction of liver disease is very important to save human life and take proper steps to control thedisease. Decision Tree algorithms have been successfully applied in various fields especially in medicalscience. This research work explores the early prediction of liver disease using various decision treetechniques. The liver disease dataset which is select for this study is consisting of attributes like totalbilirubin, direct bilirubin, age, gender, total proteins, albumin and globulin ratio. The main purpose of thiswork is to calculate the performance of various decision tree techniques and compare their performance.The decision tree techniques used in this study are J48, LMT, Random Forest, Random tree, REPTree,Decision Stump, and Hoeffding Tree. The analysis proves that Decision Stump provides the highestaccuracy than other techniques.
