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Diagnosis support using Fuzzy Cognitive Maps combined with Genetic Algorithms.

Voula C Georgopoulos¹, Chrysotomos D Stylios

¹Department of Speech and Language Therapy, Technological Educational Institute of Patras, Koukouli 26334, Patras, Greece. voula@teipat.gr

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|December 8, 2009

Summary

This study introduces a hybrid model combining Competitive Fuzzy Cognitive Maps and Genetic Algorithms for improved medical diagnosis. The new approach enhances decision-making in complex cases, particularly for speech pathology language impairments.

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Area of Science:

Computational intelligence
Medical informatics
Speech pathology

Background:

Existing medical diagnosis support systems face challenges with ambiguous decision-making.
Fuzzy Cognitive Maps (FCMs) offer a framework for modeling complex systems but require enhancement for dynamic concept interaction.
Genetic Algorithms (GAs) are powerful optimization tools applicable to complex problem-solving.

Purpose of the Study:

To develop a novel hybrid modeling methodology for enhanced medical diagnosis decision support.
To integrate Competitive Fuzzy Cognitive Maps (CFCMs) with Genetic Algorithms (GAs) for improved concept interaction.
To create more dependable Advanced Medical Diagnosis Support Systems (AMDSS) capable of handling unclear decisions.

Main Methods:

A hybrid approach combining CFCMs with GAs was developed.
A new algorithm was proposed to achieve synergy between CFCMs and GAs.
The methodology was applied to model and test a differential diagnosis problem in speech pathology.

Main Results:

The hybrid methodology successfully modeled a differential diagnosis problem.
The developed Advanced Medical Diagnosis Support Systems demonstrated enhanced dependability.
The system effectively handled situations with non-distinct decisions in language impairment diagnosis.

Conclusions:

The proposed hybrid modeling methodology offers a robust solution for complex medical diagnosis.
Integrating CFCMs with GAs significantly improves the performance of diagnosis support systems.
This approach shows promise for advancing the diagnosis of language impairments in speech pathology.