Abstract:Aim and Background:
In recent periods, micro-array data analysis using soft computing and machine learning techniques gained more interest among researchers to detect prostate cancer. Due to the small sample size of micro-array data with a larger number of attributes, traditional machine learning techniques face difficulty detecting prostate cancer.
Methodology:
The selection of relevant genes exploits useful information about micro-array data, which enhances the accuracy of detection. In this research, the … Show more
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