Authors:
K. Laxmi Narayanamma,R. V. Krishnaiah,P. Sammulal,DOI NO:
https://doi.org/10.26782/jmcms.2019.08.00003Keywords:
American Cancer Society (ACS),Machine learning (ML),Feature selection (FS),Feature extraction (FE),pancreatic cancer (PC),Abstract
Initial identification about pancreatic cancer (PC) will be a very challenging task due to particular symptoms of cancer happens only at an advanced phase & a dependable screening device to detect high danger patients. To know this challenge, a new method for decreasing the features might have been developed, tested & trained with the use of the health information of 800,114 defendants caught in the “national health interview survey (NHIS)”& Pancreatic, Colorectal, Lung, & “PLCO (ovarian cancer)” datasets, together risk of cancer might have been evaluated at a distinct level by including 18 characteristics under the recommended. The recognized “hybrid feature selection method” attained a true positive rate of 87.3 & 80.7% a true negative rate 0.86 & 0.85 for the training and testing associates, individually.Refference:
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