Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression Data
Recent studies have established the potential of classifiers designed using association rule mining methods. The current study proposes such an associative classifier to efficiently detect dengue fever using gene expression data. Labelled gene expression data has been preprocessed and discretized to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9857884/ |
| _version_ | 1811282745749405696 |
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| author | Diptaraj Sen Saubhik Paladhi Jaroslav Frnda Sankhadeep Chatterjee Soumen Banerjee Jan Nedoma |
| author_facet | Diptaraj Sen Saubhik Paladhi Jaroslav Frnda Sankhadeep Chatterjee Soumen Banerjee Jan Nedoma |
| author_sort | Diptaraj Sen |
| collection | DOAJ |
| description | Recent studies have established the potential of classifiers designed using association rule mining methods. The current study proposes such an associative classifier to efficiently detect dengue fever using gene expression data. Labelled gene expression data has been preprocessed and discretized to mine association rules using well-established rule mining methods. Thereafter, unsupervised clustering methods have been applied to the discretized gene expression data to reduce and select the most promising features. The final feature reduced discretized gene expression data is subsequently used to mine rules in order to classify subjects into ‘Dengue Fever’ or ‘Healthy’. Two well-known association rule mining methods, viz., Apriori and FP-Growth, have been used here along with various types of well established clustering methods. Extensive analysis has been reported with performance parameters in terms of accuracy, precision, recall and false positive rate using 5-fold cross-validation. Furthermore, a separate investigation has been conducted to find the most suitable number of features and confidence of association rule mining methods. The experimental results obtained indicate accurate detection of dengue fever patients at an early stage using the proposed associative classification method. |
| first_indexed | 2024-04-13T01:58:04Z |
| format | Article |
| id | doaj.art-4b817b5e57174e6193cd8c57b189b66d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T01:58:04Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4b817b5e57174e6193cd8c57b189b66d2022-12-22T03:07:42ZengIEEEIEEE Access2169-35362022-01-0110883408835310.1109/ACCESS.2022.31989379857884Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression DataDiptaraj Sen0Saubhik Paladhi1Jaroslav Frnda2https://orcid.org/0000-0001-6065-3087Sankhadeep Chatterjee3https://orcid.org/0000-0002-3930-4699Soumen Banerjee4https://orcid.org/0000-0003-3918-1892Jan Nedoma5https://orcid.org/0000-0001-7459-2043Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, IndiaDepartment of Computer Science and Engineering, University of Kalyani, Kalyani, IndiaDepartment of Telecommunications, VSB—Technical University of Ostrava, Ostrava, Czech RepublicDepartment of Computer Science and Technology, University of Engineering and Management, Kolkata, IndiaDepartment of Electronics and Communication Engineering, University of Engineering and Management, Kolkata, IndiaDepartment of Telecommunications, VSB—Technical University of Ostrava, Ostrava, Czech RepublicRecent studies have established the potential of classifiers designed using association rule mining methods. The current study proposes such an associative classifier to efficiently detect dengue fever using gene expression data. Labelled gene expression data has been preprocessed and discretized to mine association rules using well-established rule mining methods. Thereafter, unsupervised clustering methods have been applied to the discretized gene expression data to reduce and select the most promising features. The final feature reduced discretized gene expression data is subsequently used to mine rules in order to classify subjects into ‘Dengue Fever’ or ‘Healthy’. Two well-known association rule mining methods, viz., Apriori and FP-Growth, have been used here along with various types of well established clustering methods. Extensive analysis has been reported with performance parameters in terms of accuracy, precision, recall and false positive rate using 5-fold cross-validation. Furthermore, a separate investigation has been conducted to find the most suitable number of features and confidence of association rule mining methods. The experimental results obtained indicate accurate detection of dengue fever patients at an early stage using the proposed associative classification method.https://ieeexplore.ieee.org/document/9857884/Gene expression dataassociation rulesApriori algorithmFP-growth algorithmclustering |
| spellingShingle | Diptaraj Sen Saubhik Paladhi Jaroslav Frnda Sankhadeep Chatterjee Soumen Banerjee Jan Nedoma Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression Data IEEE Access Gene expression data association rules Apriori algorithm FP-growth algorithm clustering |
| title | Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression Data |
| title_full | Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression Data |
| title_fullStr | Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression Data |
| title_full_unstemmed | Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression Data |
| title_short | Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression Data |
| title_sort | associative classifier coupled with unsupervised feature reduction for dengue fever classification using gene expression data |
| topic | Gene expression data association rules Apriori algorithm FP-growth algorithm clustering |
| url | https://ieeexplore.ieee.org/document/9857884/ |
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