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Welcome to JJEM: A Multi-Disicplinary Journal of JNNCE, Shimoga

JJEM Ninth Issue - Volume 5 Number 1 -2021

Volume 5, Issue 1


Retraction: A Comparative Analysis of Classification Algorithms in Liver Disease Detection


Due to copyright issue, This Article has been Retracted from the JJEM journal- Vol 5, No.1 on 2021-12-15

Published:    2021-09-30


Authors


Akanksha Soni,  Anivash Rai


Abstract


Liver is the prime organ in the human body and executes a number of functions related to producing necessary proteins, carbohydrates metabolism, energy storage, and detoxification of waste. Diseases that may affect the liver include hepatitis, fatty liver, bleeding, fatigue, jaundice. Liver disease affects millions of the populace around the earth and diagnosis at an initial phase is very essential for better treatment. It is an extremely difficult task for medical researchers to predict the infection in the prior stages owing to subtle symptoms. So there is a solid requirement for an efficient, accurate, and practical framework to predict the consequence of such infection. It will be valuable for taking precautions and appropriate treatment. To reduce this concern, we proposed a system that is capable enough to detect liver disease using machine learning approaches. The major intention of this work is to use classification algorithms to identify the liver patients from healthy individuals with the help of automated processes to overcome human fault and decrease the false alarm rate that gives the progression which is more accurate, effortless, and unproblematic. This project also aims to compare the categorization algorithms that depend on their performance features. To gain results with less processing instance and a high precision rate. In this work, four classification algorithms that are Logistic Regression, Random Forest (RF), K Nearest Neighbor (KNN), and Decision Tree have been applied for analyzing their performance which is based on the liver patient data. The dataset contains 583 liver patient’s data whereas 75.64% male patients and 24.36% are female patients.


Keywords


Liver Disease; Machine Learning; Classification Algorithms; Logistic Regression; Random Forest; Decision Tree; K -Nearest Neighbor (KNN)