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Strategic Analysis in Prediction of Liver Disease Using Different Classification Algorithms
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Author(s): Binish Khan (University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India), Piyush Kumar Shukla (University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India), Manish Kumar Ahirwar (University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India)and Manish Mishra (University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India)
Copyright: 2021
Pages: 13
Source title:
Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning
Source Author(s)/Editor(s): Geeta Rani (Manipal University Jaipur, India)and Pradeep Kumar Tiwari (Manipal University Jaipur, India)
DOI: 10.4018/978-1-7998-2742-9.ch022
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Abstract
Liver diseases avert the normal activity of the liver. Discovering the presence of liver disorder at an early stage is a complex task for the doctors. Predictive analysis of liver disease using classification algorithms is an efficacious task that can help the doctors to diagnose the disease within a short duration of time. The main motive of this study is to analyze the parameters of various classification algorithms and compare their predictive accuracies so as to find the best classifier for determining the liver disease. This chapter focuses on the related works of various authors on liver disease such that algorithms were implemented using Weka tool that is a machine learning software written in Java. Also, orange tool is utilized to compare several classification algorithms in terms of accuracy. In this chapter, random forest, logistic regression, and support vector machine were estimated with an aim to identify the best classifier. Based on this study, random forest with the highest accuracy outperformed the other algorithms.
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