Detection of Liver Disease Using Machine Learning

Authors

  • Ashlin Rodrigues,Shruti Suresh,Akarsh Ghale, Prof. Venkataravana Nayak K

Abstract

Liver disease accounts for around 2 million deaths worldwide every year. Early and efficient detection of liver disease can vastly aid the course of treatment. This paper aims to improve liver disease detection systems using machine learning by applying Tree-based feature selection, which identifies the most relevant attributes to be used in the model. It also compares 3 commonly used machine learning models (Logistic Regression, Naïve Bayes, Random Forest algorithm) and determines the best model for this application. The training times for each model, both with and without feature selection, have been obtained and compared.

Published

2022-06-30

How to Cite

Ashlin Rodrigues,Shruti Suresh,Akarsh Ghale, Prof. Venkataravana Nayak K. (2022). Detection of Liver Disease Using Machine Learning. International Journal of Advanced Engineering Science and Information Technology, 10(6), 96–104. Retrieved from http://ijaesit.com/index.php/home/article/view/102