A Novel Human Diabetes Biomarker Recognition Approach Using Fuzzy Rough Multigranulation Nearest Neighbour Classifier Mo
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ORIGINAL RESEARCH ARTICLE
A Novel Human Diabetes Biomarker Recognition Approach Using Fuzzy Rough Multigranulation Nearest Neighbour Classifier Model Swarup Kr Ghosh1 · Anupam Ghosh2 Received: 9 May 2020 / Revised: 22 August 2020 / Accepted: 31 August 2020 © International Association of Scientists in the Interdisciplinary Areas 2020
Abstract The selection of gene identifier from microarray databases is a challenging task since microarray contains large number of gene attributes for a few samples. This article proposes a novel fuzzy-rough set-based gene expression features selection using fuzzy-rough reduct under multi-granular space for human diabetes patient. Firstly, fuzzy multi-granular gain has been computed from the expression datasets via fuzzy entropy which reduces the dimension of the database. Thereafter, the features have been selected from microarray using the fuzzy rough reduct and information gain with respect to their expression patterns. To reduce the computational cost, a decision making scheme has been designed using a rough approximation of a fuzzy concept in the field of multi-granulation framework. Finally, we have recognized the association among the genomes that have expressively different expression patterns from controlled state to the diabetic state with respect to their impression using modified fuzzy-rough nearest neighbour classifier (FRNNC). Five standard diabetic microarray datasets have been considered to quantify the efficiency of the designed FRNNC model and are validated with F measure using diabetes gene expression NCBI database and it performs superior compared to existing methods. Keywords Diabetes · Microarray · Fuzzy-rough sets · Fuzzy-rough reduct · Fuzzy-rough nearest neighbour · Information gain
1 Introduction Diabetes mellitus (DM) is considered as a deadly disease affecting the worldwide population next to cancer. Around 415 million people were affected by DM in the year 2015 and it may increase to 642 million by 2040 as per International Diabetes Federation. Diabetes comprises metabolic diseases resulting from insufficiency in insulin emission from pancreas [1–3]. Diabetes can be classified into two broad groups: type 1 and type 2. The type 1 diabetes (insulin-dependent diabetes) is a chronic condition resulting from autoimmune-mediated cellular devastation of the beta cells of the pancreas [2, 4]. In type 2 diabetes or adult-onset * Swarup Kr Ghosh [email protected] Anupam Ghosh [email protected] 1
Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, India
Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India
2
diabetes 90% cases occurs individual who has insulin resistance and has relative insulin insufficiency [3]. It is usually allied with a strong genetic tendency [3, 4]. In microarray technology, gene expression data are extracted from RNA sequences with a frame of reference and it is organized in row-column format i.e., a 2D matrix. The microarray is nothing but
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