Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
Soft computing for medical diagnosis in field of computer science has been a syndicate of methodologies. These all work together in order to provide a facility to make decisions from consistent data or experience of experts of related fields. Many artificial intelligence techniques such as fuzzy logic, neural network, genetic algorithm, etc. or integration of those may be used in the field of medical science. These types of methodologies have also been incorporated in order to diagnose the lung cancer disease. The main objective of this study to develop a fuzzy expert system with number of linguistic variables as fuzzy feature sets along with different membership functions to depict the risk factor in lung cancer disease. The risk level of disease is decided by the rule set of the fuzzy system. For improving the accuracy of the system, we have designed two stage fuzzy expert system with input of six features at each stage. The experimental results make obvious that the proposed work has enhanced accuracy with reduced computing time.
How to cite this article:
N.P. Gopalan and A. Malathi. Twin Stage Fuzzy Expert System Modeling for Lung Cancer Risk Diagnosis.
DOI: https://doi.org/10.36478/ajit.2017.552.560
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2017.552.560