Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset.
Yamid Fabián Hernández-Julio1, Leonardo Antonio Díaz-Pertuz1, Martha Janeth Prieto-Guevara2
1Faculty of Economics, Administrative and Accounting Sciences, Universidad del Sinú Elías Bechara Zainúm, Montería 230002, Colombia.
Summary
This study introduces fuzzy set theory for clinical decision support systems (CDSSs) to improve breast cancer diagnosis. The developed fuzzy inference systems (FIS) demonstrated superior precision compared to existing literature methods.
Area of Science:
- Computer Science
- Artificial Intelligence
- Medical Informatics
Background:
- Clinical Decision Support Systems (CDSSs) are crucial for informed medical decision-making.
- Developing effective CDSSs requires robust knowledge databases and rule bases.
- Fuzzy set theory offers a powerful framework for handling uncertainty in medical data.
Purpose of the Study:
- To implement and validate Mamdani-type fuzzy set theory for clinical decision support systems.
- To evaluate the performance of fuzzy inference systems (FIS) in categorizing the Wisconsin breast cancer dataset.
- To compare the precision of developed FIS against existing literature methods.
Main Methods:
- Utilized Mamdani-type fuzzy set theory.
- Employed clustering and dynamic tables for system development.
- Applied fuzzy inference systems (FIS) with varying input features.
- Validated outcomes against the Wisconsin breast cancer dataset.
Main Results:
- The developed fuzzy inference systems (FIS) successfully categorized breast cancer data.
- Performance metrics for the proposed FIS surpassed those reported in existing literature.
- Demonstrated superior precision in classifying the output variable for breast cancer diagnosis.
Conclusions:
- Mamdani-type fuzzy set theory provides a validated and effective approach for clinical decision support.
- The implemented fuzzy systems show enhanced precision for breast cancer dataset categorization.
- This research highlights the potential of fuzzy logic in improving diagnostic accuracy in medical informatics.
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