Computative Analysis of Various Techniques for Classification of Liver Disease

This paper presents a software-engineered approach using a classification algorithm for the classification of liver disease. The ILPD dataset is used for the proposed work. Different attributes of liver patient records such as direct bilirubin, age, sex, total bilirubin, alphos, albumin, sgpt, globu...

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Bibliographic Details
Published in:Journal of physics. Conference series 2023-03, Vol.2466 (1), p.12035
Main Authors: Sujith, J., Kumar, P. Karthik, Reddy, S. Joshi Manohar, Kanhe, Aniruddha
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Classification
Datasets
Liver
Physics
Software engineering
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Summary:This paper presents a software-engineered approach using a classification algorithm for the classification of liver disease. The ILPD dataset is used for the proposed work. Different attributes of liver patient records such as direct bilirubin, age, sex, total bilirubin, alphos, albumin, sgpt, globulin ratio, sgot are used to classify liver disease. The proposed Convolution Neural Network classification technique shows an accuracy of 67% and a precision of 71%. Various classification algorithms such as CNN, RNN, ANN, and logistic regression are executed on the liver patient dataset and their accuracy is determined.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2466/1/012035