A hybrid GA-based fuzzy classifying approach to urinary analysis modeling

Authors:
Ping Wu;Erik D. Goodman;Tang Jiang;Min Pei
Affiliations:
East China Normal University, Shanghai, China;Michigan State University, East Lansing, MI, USA;The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China;Michigan State University, East Lansing, USA
Venue:
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Year:
2009

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Abstract

Automatically analyzing urine samples is a very important issue in laboratory practice. In this paper, a hybrid GA-based fuzzy classification technique is proposed to create fuzzy rules for further identifying and monitoring diseases of the kidney and urinary tract. Fuzzy genetic learning has proven to be a promising approach and widely used to carry out medical diagnoses today. We have evaluated the classification performance of the different genetic fuzzy rule learning approaches. Results show that our proposed hybrid GA-based fuzzy learning system provides better classification accuracy and generates symbolic rules which outperform the previous GA-based fuzzy approaches.