An interpretable fuzzy rule-based classification methodology for medical diagnosis.
OBJECTIVE
The aim of this paper is to present a novel fuzzy classification framework for the automatic extraction of fuzzy rules from labeled numerical data, for the development of efficient medical diagnosis systems.


METHODS
The proposed methodology focuses on the accuracy and interpretability of the generated knowledge that is produced by an iterative, flexible and meaningful input partitioning mechanism. The generated hierarchical fuzzy rule structure is composed by linguistic; multiple consequent fuzzy rules that considerably affect the model comprehensibility.


CONCLUSIONS
The performance of the proposed method is tested on three medical pattern classification problems and the obtained results are compared against other existing methods. It is shown that the proposed variable input partitioning leads to a flexible decision making framework and fairly accurate results with a small number of rules and a simple, fast and robust training process.
Gadaras
Ioannis
I
University of Manchester, School of Computer Science, Manchester, United Kingdom. i.gadaras@student.manchester.ac.uk
Mikhailov
Ludmil
L
D000465:Algorithms; D001130:Arizona / Q000453:epidemiology; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000453:epidemiology; D002965:Classification / Q000379:methods*; D003625:Data Collection; D003920:Diabetes Mellitus / Q000175:diagnosis / Q000453:epidemiology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D017143:Fuzzy Logic; D005823:Genetics; D006403:Hematologic Tests / Q000706:statistics & numerical data; D006801:Humans; D007198:Indians, North American; D008111:Liver Function Tests; D011247:Pregnancy; D015203:Reproducibility of Results; D055815:Young Adult