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...
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MDPI AG
2023-02-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/11/2/581 |
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| author | Abdul Quadir Md Sanika Kulkarni Christy Jackson Joshua Tejas Vaichole Senthilkumar Mohan Celestine Iwendi |
| author_facet | Abdul Quadir Md Sanika Kulkarni Christy Jackson Joshua Tejas Vaichole Senthilkumar Mohan Celestine Iwendi |
| author_sort | Abdul Quadir Md |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T09:07:25Z |
| format | Article |
| id | doaj.art-e2d3e1bfb8d94ecc8808b982abd673f6 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-11T09:07:25Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-e2d3e1bfb8d94ecc8808b982abd673f62023-11-16T19:20:22ZengMDPI AGBiomedicines2227-90592023-02-0111258110.3390/biomedicines11020581Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver DiseaseAbdul Quadir Md0Sanika Kulkarni1Christy Jackson Joshua2Tejas Vaichole3Senthilkumar Mohan4Celestine Iwendi5School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Creative Technologies, University of Bolton, Bolton BL3 5AB, UKThere 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.https://www.mdpi.com/2227-9059/11/2/581liver diseasemachine learningmultivariate imputationfeature scalingensemble learninggradient boosting |
| spellingShingle | Abdul Quadir Md Sanika Kulkarni Christy Jackson Joshua Tejas Vaichole Senthilkumar Mohan Celestine Iwendi Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease Biomedicines liver disease machine learning multivariate imputation feature scaling ensemble learning gradient boosting |
| 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 | liver disease machine learning multivariate imputation feature scaling ensemble learning gradient boosting |
| url | https://www.mdpi.com/2227-9059/11/2/581 |
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