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Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease
There has been a sharp increase in liver disease globally, and many people are dying without even knowing that they have it. As a result of its limited symptoms, it is extremely difficult to detect liver disease until the very last stage. In the event of early detection, patients can begin treatment...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953600/ https://www.ncbi.nlm.nih.gov/pubmed/36831118 http://dx.doi.org/10.3390/biomedicines11020581 |
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author | Md, Abdul Quadir Kulkarni, Sanika Joshua, Christy Jackson Vaichole, Tejas Mohan, Senthilkumar Iwendi, Celestine |
author_facet | Md, Abdul Quadir Kulkarni, Sanika Joshua, Christy Jackson Vaichole, Tejas Mohan, Senthilkumar Iwendi, Celestine |
author_sort | Md, Abdul Quadir |
collection | PubMed |
description | There has been a sharp increase in liver disease globally, and many people are dying without even knowing that they have it. As a result of its limited symptoms, it is extremely difficult to detect liver disease until the very last stage. In the event of early detection, patients can begin treatment earlier, thereby saving their lives. It has become increasingly popular to use ensemble learning algorithms since they perform better than traditional machine learning algorithms. In this context, this paper proposes a novel architecture based on ensemble learning and enhanced preprocessing to predict liver disease using the Indian Liver Patient Dataset (ILPD). Six ensemble learning algorithms are applied to the ILPD, and their results are compared to those obtained with existing studies. The proposed model uses several data preprocessing methods, such as data balancing, feature scaling, and feature selection, to improve the accuracy with appropriate imputations. Multivariate imputation is applied to fill in missing values. On skewed columns, log1p transformation was applied, along with standardization, min–max scaling, maximum absolute scaling, and robust scaling techniques. The selection of features is carried out based on several methods including univariate selection, feature importance, and correlation matrix. These enhanced preprocessed data are trained on Gradient boosting, XGBoost, Bagging, Random Forest, Extra Tree, and Stacking ensemble learning algorithms. The results of the six models were compared with each other, as well as with the models used in other research works. The proposed model using extra tree classifier and random forest, outperformed the other methods with the highest testing accuracy of 91.82% and 86.06%, respectively, portraying our method as a real-world solution for detecting liver disease. |
format | Online Article Text |
id | pubmed-9953600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99536002023-02-25 Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease Md, Abdul Quadir Kulkarni, Sanika Joshua, Christy Jackson Vaichole, Tejas Mohan, Senthilkumar Iwendi, Celestine Biomedicines Article There has been a sharp increase in liver disease globally, and many people are dying without even knowing that they have it. As a result of its limited symptoms, it is extremely difficult to detect liver disease until the very last stage. In the event of early detection, patients can begin treatment earlier, thereby saving their lives. It has become increasingly popular to use ensemble learning algorithms since they perform better than traditional machine learning algorithms. In this context, this paper proposes a novel architecture based on ensemble learning and enhanced preprocessing to predict liver disease using the Indian Liver Patient Dataset (ILPD). Six ensemble learning algorithms are applied to the ILPD, and their results are compared to those obtained with existing studies. The proposed model uses several data preprocessing methods, such as data balancing, feature scaling, and feature selection, to improve the accuracy with appropriate imputations. Multivariate imputation is applied to fill in missing values. On skewed columns, log1p transformation was applied, along with standardization, min–max scaling, maximum absolute scaling, and robust scaling techniques. The selection of features is carried out based on several methods including univariate selection, feature importance, and correlation matrix. These enhanced preprocessed data are trained on Gradient boosting, XGBoost, Bagging, Random Forest, Extra Tree, and Stacking ensemble learning algorithms. The results of the six models were compared with each other, as well as with the models used in other research works. The proposed model using extra tree classifier and random forest, outperformed the other methods with the highest testing accuracy of 91.82% and 86.06%, respectively, portraying our method as a real-world solution for detecting liver disease. MDPI 2023-02-16 /pmc/articles/PMC9953600/ /pubmed/36831118 http://dx.doi.org/10.3390/biomedicines11020581 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Md, Abdul Quadir Kulkarni, Sanika Joshua, Christy Jackson Vaichole, Tejas Mohan, Senthilkumar Iwendi, Celestine Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease |
title | Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease |
title_full | Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease |
title_fullStr | Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease |
title_full_unstemmed | Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease |
title_short | Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease |
title_sort | enhanced preprocessing approach using ensemble machine learning algorithms for detecting liver disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953600/ https://www.ncbi.nlm.nih.gov/pubmed/36831118 http://dx.doi.org/10.3390/biomedicines11020581 |
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