Medical & Biological Engineering & Computing

, Volume 56, Issue 4, pp 709–720 | Cite as

Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data

  • Sankhadeep ChatterjeeEmail author
  • Nilanjan Dey
  • Fuqian Shi
  • Amira S. Ashour
  • Simon James Fong
  • Soumya Sen
Original Article


Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.


Dengue fever Bag-of-features Modified cuckoo search Artificial neural networks Gene expression data Incremental feature selection scheme 


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Copyright information

© International Federation for Medical and Biological Engineering 2017

Authors and Affiliations

  • Sankhadeep Chatterjee
    • 1
    Email author
  • Nilanjan Dey
    • 2
  • Fuqian Shi
    • 3
  • Amira S. Ashour
    • 4
  • Simon James Fong
    • 5
  • Soumya Sen
    • 6
  1. 1.Department of Computer Science & EngineeringUniversity of CalcuttaKolkataIndia
  2. 2.Department of Information TechnologyTechno India College of TechnologyKolkataIndia
  3. 3.College of Information and EngineeringWenzhou Medical UniversityWenzhouPeople’s Republic of China
  4. 4.Department of Electronics and Electrical Communications Engineering, Faculty of EngineeringTanta UniversityTantaEgypt
  5. 5.Department of Computer and Information Science Data Analytics and Collaborative Computing LaboratoryUniversity of MacauMacauPeople’s Republic of China
  6. 6.A.K. Choudhury School of Information TechnologyUniversity of CalcuttaKolkataIndia

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