Classification of liver disease diagnosis: a comparative study
Medical Data Mining (MDM) is one of the most critical aspects of automated disease diagnosis and disease prediction. MDM involves developing data mining algorithms and techniques to analyze medical data. In recent years, liver disorders have excessively increased and liver diseases are becoming one...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
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IEEE (IEEEXplore)
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/41298/ |
| _version_ | 1848849658383695872 |
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| author | Bahramirad, Sina Mustapha, Aida Eshraghi, Maryam |
| author_facet | Bahramirad, Sina Mustapha, Aida Eshraghi, Maryam |
| author_sort | Bahramirad, Sina |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Medical Data Mining (MDM) is one of the most critical aspects of automated disease diagnosis and disease prediction. MDM involves developing data mining algorithms and techniques to analyze medical data. In recent years, liver disorders have excessively increased and liver diseases are becoming one of the most fatal diseases in several countries. In this study, two real liver patient datasets were investigated for building classification models in order to predict liver diagnosis. Eleven data mining classification algorithms were applied to the datasets and the performance of all classifiers are compared against each other in terms of accuracy, precision, and recall. Several investigations have also been carried out to improve performance of the classification models. Finally, the results shown promising methodology in diagnosing liver disease during the earlier stages. |
| first_indexed | 2025-11-15T09:53:54Z |
| format | Conference or Workshop Item |
| id | upm-41298 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:53:54Z |
| publishDate | 2013 |
| publisher | IEEE (IEEEXplore) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-412982015-11-03T03:19:49Z http://psasir.upm.edu.my/id/eprint/41298/ Classification of liver disease diagnosis: a comparative study Bahramirad, Sina Mustapha, Aida Eshraghi, Maryam Medical Data Mining (MDM) is one of the most critical aspects of automated disease diagnosis and disease prediction. MDM involves developing data mining algorithms and techniques to analyze medical data. In recent years, liver disorders have excessively increased and liver diseases are becoming one of the most fatal diseases in several countries. In this study, two real liver patient datasets were investigated for building classification models in order to predict liver diagnosis. Eleven data mining classification algorithms were applied to the datasets and the performance of all classifiers are compared against each other in terms of accuracy, precision, and recall. Several investigations have also been carried out to improve performance of the classification models. Finally, the results shown promising methodology in diagnosing liver disease during the earlier stages. IEEE (IEEEXplore) 2013 Conference or Workshop Item NonPeerReviewed Bahramirad, Sina and Mustapha, Aida and Eshraghi, Maryam (2013) Classification of liver disease diagnosis: a comparative study. In: 2013 Second International Conference on Informatics and Applications (ICIA), 23-25 Sept. 2013, Poland. (pp. 42-46). 10.1109/ICoIA.2013.6650227 |
| spellingShingle | Bahramirad, Sina Mustapha, Aida Eshraghi, Maryam Classification of liver disease diagnosis: a comparative study |
| title | Classification of liver disease diagnosis: a comparative study |
| title_full | Classification of liver disease diagnosis: a comparative study |
| title_fullStr | Classification of liver disease diagnosis: a comparative study |
| title_full_unstemmed | Classification of liver disease diagnosis: a comparative study |
| title_short | Classification of liver disease diagnosis: a comparative study |
| title_sort | classification of liver disease diagnosis: a comparative study |
| url | http://psasir.upm.edu.my/id/eprint/41298/ http://psasir.upm.edu.my/id/eprint/41298/ |