Predicting liver disease using automatic classification model

Liver disease records around two million per annum worldwide. One million owing to cirrhosis and one Million owing to viral hepatitis and hepatocellular carcinoma. Presently, Cirrhosis is the eleventh most common cause of death and liver cancer is the leading cause of death. To uphold the treatment...

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Bibliographic Details
Main Authors: Gajendran, G., Priyanka, R.
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Back propagation networks
Machine learning
Online Access:Get full text
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Summary:Liver disease records around two million per annum worldwide. One million owing to cirrhosis and one Million owing to viral hepatitis and hepatocellular carcinoma. Presently, Cirrhosis is the eleventh most common cause of death and liver cancer is the leading cause of death. To uphold the treatment it is necessary to detect the cause and range of liver damage. In this study, a group of blood tests called liver function tests (LFTs) is utilized to diagnose the liver disease. To contour the diagnostic method in day to day life and avoid misdiagnosis, strategies of artificial intelligence can be employed. The objective of this study is to enhance the accuracy in LFTs datasets using Artificial Neural Network ( machine learning) . Back propagation algorithm is used in this study as leading learner. The experimental results exhibit accuracy of 74%.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0110047