  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.
