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Liver Disease Prediction using Machine Learning Algorithms

by Riya, Barinderjit Kaur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 27
Year of Publication: 2023
Authors: Riya, Barinderjit Kaur
10.5120/ijca2023923022

Riya, Barinderjit Kaur . Liver Disease Prediction using Machine Learning Algorithms. International Journal of Computer Applications. 185, 27 ( Aug 2023), 36-44. DOI=10.5120/ijca2023923022

@article{ 10.5120/ijca2023923022,
author = { Riya, Barinderjit Kaur },
title = { Liver Disease Prediction using Machine Learning Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2023 },
volume = { 185 },
number = { 27 },
month = { Aug },
year = { 2023 },
issn = { 0975-8887 },
pages = { 36-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number27/32863-2023923022/ },
doi = { 10.5120/ijca2023923022 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:13.676723+05:30
%A Riya
%A Barinderjit Kaur
%T Liver Disease Prediction using Machine Learning Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 27
%P 36-44
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Early diagnosis is essential to improving patient outcomes and decreasing the costs of healthcare. Liver disease is a big worldwide health problem, and early detection plays a critical role in both of these areas. A data-driven technique that makes use of supervised learning algorithms is presented in this research report as a method for estimating the likelihood of developing liver disease. For the objectives of research, the study makes use of a dataset collected from www.kaggle.com that contains information on the demographics, lifestyle, and medical history of 416 patients who were treated at a hospital in India. In order to construct accurate prediction models for the risk of liver illness, we use three different supervised learning techniques. These are decision trees, random forests, and logistic regressions. Accuracy, specificity, sensitivity, and the area under the receiver operating characteristic (ROC) curve are the metrics that are used in order to assess the performance of these models. According to the findings, the hybrid method surpasses the other two algorithms by obtaining higher levels of accuracy (75.7 percent), sensitivity (71.4 percent), and specificity (77.2 percent), as well as a higher area under the ROC curve (0.80). This work demonstrates the potential of supervised learning algorithms in forecasting the risk of liver disease using patient data, especially in areas where there is a limited availability of resources.

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Index Terms

Computer Science
Information Sciences

Keywords

Liver Disease Chronic diseases data analysis Machine Learning Algorithms

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