A Novel Clustering-Based Gene Expression Pattern Analysis for Human Diabetes Patients Using Intuitionistic Fuzzy Set and Multigranulation Rough Set Model

  • Swarup Kr GhoshEmail author
  • Anupam Ghosh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1154)


In this article, we present an intuitionistic fuzzy set (IFS)-based gene expression pattern classification using multigranulation rough set theory for human diabetes patient. At the very beginning, the proposed scheme generates a soft-information structure from the microarray by IFS via multiple fuzzy membership functions with Yager generating function. The intuitionistic fuzzy set deals with the ambiguity between normal state and diabetic state from gene expression microarray via the hesitation degree while shaping the membership function. Thereafter, a multigranulation rough set is utilized for the measurement of accuracy and roughness from expression pattern that has been deferentially expressed from normal state to diabetic state. Lastly, Rough-fuzzy C-means clustering has been applied on the datasets into two clusters such as diabetes or non-diabetes. The associations among human genes have also been identified which are correlated with diabetes (type-2). In addition, we have validated a measurement by F-score using diabetes gene expression NCBI database and achieved better performance in comparison with baseline methods.


Diabetes gene microarray Rough set Multigranulation Intuitionistic fuzzy set Rough-fuzzy c-means 


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSister Nivedita UniversityKolkataIndia
  2. 2.Department of Computer Science and EngineeringNetaji Subhash Engineering CollegeKolkataIndia

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