Prediction of Liver Disease using Regression Tree


  • Vinutha M.R. Malnad College of Engineering, Hassan
  • Chandrika J. Malnad College of Engineering , Hassan



Decision Tree, Regression Tree, Liver Cirrhosis, Machine Learning.


Abstract— Data Mining plays a decisive role especially in medical domain. Decision trees are predominant model in machine learning. Decision trees are simple and very effective classification approach. The decision tree identifies the utmost prime features of a given problem. One of the most common disease in India is Liver Cirrhosis. It is distinctly difficult to uncover Liver Cirrhosis in its initial stage. However early diagnosis of Liver Cirrhosis is highly important.The liver disease data set has a collection of distinguishing features that affect the healthy state of a patient. Machine Learning methods enable knowledge acquisition in early stages and use of this acquired knowledge plays an important role in solving problems like suppose if we want to predict whether the patient with the Liver Cirrhosis has also been suffering from Hepatitis C or not. In order to easily arrive at this knowledge certainly there is a need for fully integrated system. In this paper the collected Liver disease data set is analyzed and prognosticated whether the patient is suffering from liver cirrhosis or not.


Author Biographies

Vinutha M.R., Malnad College of Engineering, Hassan

Assistant Professor

Information Science and Engineering

Chandrika J., Malnad College of Engineering , Hassan


Computer Science and Engineering




How to Cite

M.R., V., & J., C. (2021). Prediction of Liver Disease using Regression Tree. International Journal of Online and Biomedical Engineering (iJOE), 17(02), pp. 164–172.



Short Papers