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Health Care Automation in Compliance to Industry 4.0 Standards: A Case Study of Liver Disease Prediction

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Title Health Care Automation in Compliance to Industry 4.0 Standards: A Case Study of Liver Disease Prediction
 
Creator Venkata, Manjula Devarakonda
Lingamgunta, Sumalatha
Murali, K
 
Subject Advanced soft-computing techniques
Indian dataset
Industrial internet
Machine learning
RF algorithm
 
Description 263-268
The industrial internet contributes to the standards of Industry 4.0, which involve handling large volumes of data using
advanced soft-computing techniques. Machine Learning (ML) is an advanced soft-computing technique that plays a critical
role in predicting and detecting serial chronic diseases, thereby automating the diagnosis. The process constitutes and uses
several data mining algorithms and methods for efficient medical data analysis. Recent studies on several chronic diseases,
liver disorders and diseases associated with the organ have been fatal. In this paper, the liver patient dataset from India is
considered and investigated for developing a classification model. Liver disease is a dangerous, life-threatening disease
often diagnosed false positive. Mild liver enlargement, improper or ambiguous functionality over a brief period, is
prominent even in healthy people, which has become the main reason for ignoring the same at the early stage. It is essential
to predict liver disease through the parameters and their values from the liver functionality test sensing the behavior of
similar patients who were ignored in the initial stage. In this paper, the machine learning technique is demonstrated to
predict liver disease using the liver function test data of the 580 patients as training data. The model has been developed
with an accuracy of approximately 75%. The simulation-based experiment is based on the publicly available dataset and can
be extended to any native set to predict the patients' health quickly. The Random Forest Algorithm is used to develop the
model in Matlab, and the analysis is carried out using parameters like total bilirubin, alkaline phosphotase, alamine
aminotransferase, total proteins, and A/G ratio.
 
Date 2023-02-08T05:03:41Z
2023-02-08T05:03:41Z
2023-02
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61357
https://doi.org/10.56042/jsir.v82i2.70215
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(02) [February 2023]