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

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.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Design, Experimentation, Performance

Keywords:
feature selection, fuzzy rule learning, genetic algorithms, urinalysis