Paper Info
Title
Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression Data
Abstract
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.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3198937
IEEE ACCESS
Keywords
DocType
Volume
Clustering algorithms, Gene expression, Data mining, Viruses (medical), Classification algorithms, Itemsets, Biology, Clustering methods, Gene expression data, association rules, Apriori algorithm, FP-growth algorithm, clustering
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Diptaraj Sen100.34
Saubhik Paladhi200.34
Jaroslav Frnda388.70
Sankhadeep Chatterjee400.34
Soumen Banerjee500.68
Jan Nedoma600.68