Abstract
In this paper, a neuro-fuzzy based classification technique is adopted to efficiently deal with the classification problem of the heterogeneous medical data sets. The Proposed classification technique is based on the neuro-fuzzy classification (NE-FCLASS) system. However, several improvements are introduced to the origin NEFCLASS. The motivation of this work is triggered by the fact that most conventional classification techniques are capable of handling the numeric data sets but not the heterogenous ones. This can be seen in the classification of the medical data sets. This paper tackles the data classification problem of two medical diseases. The first data set, which is a numeric data set, is related to the Wisconsin breast cancer diagnosis. The second is a heterogeneous data set and is the Wisconsin heart disease diagnosis. Experimental results demonstrate that the proposed technique can effectively improve the classification performance of heterogenous data sets.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.