Fuzzy Gene Selection and Cancer Classification Based on Deep Learning Model

• URL: http://arxiv.org/abs/2305.04883v1
• Date: Thu, 4 May 2023 21:52:57 GMT
• Title: Fuzzy Gene Selection and Cancer Classification Based on Deep Learning Model
• Authors: Mahmood Khalsan, Mu Mu, Eman Salih Al-Shamery, Lee Machado, Suraj Ajit, Michael Opoku Agyeman
• Abstract summary: We developed a new fuzzy gene selection technique (FGS) to identify informative genes to facilitate cancer classification. With our FGS-enhanced method, the cancer classification model achieved 96.5%,96.2%,96%, and 95.9% for accuracy, precision, recall, and f1-score respectively. In examining the six datasets that were used, the proposed model demonstrates it's capacity to classify cancer effectively.
• Score: 1.3072222152900117
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Machine learning (ML) approaches have been used to develop highly accurate and efficient applications in many fields including bio-medical science. However, even with advanced ML techniques, cancer classification using gene expression data is still complicated because of the high dimensionality of the datasets employed. We developed a new fuzzy gene selection technique (FGS) to identify informative genes to facilitate cancer classification and reduce the dimensionality of the available gene expression data. Three feature selection methods (Mutual Information, F-ClassIf, and Chi-squared) were evaluated and employed to obtain the score and rank for each gene. Then, using Fuzzification and Defuzzification methods to obtain the best single score for each gene, which aids in the identification of significant genes. Our study applied the fuzzy measures to six gene expression datasets including four Microarray and two RNA-seq datasets for evaluating the proposed algorithm. With our FGS-enhanced method, the cancer classification model achieved 96.5%,96.2%,96%, and 95.9% for accuracy, precision, recall, and f1-score respectively, which is significantly higher than 69.2% accuracy, 57.8% precision, 66% recall, and 58.2% f1-score when the standard MLP method was used. In examining the six datasets that were used, the proposed model demonstrates it's capacity to classify cancer effectively.

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