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Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems.
Yamid Fabián Hernández-Julio1, Martha Janeth Prieto-Guevara2, Wilson Nieto-Bernal3
1Facultad de Ciencias Económicas, Administrativas y Contables, Universidad del Sinú Elías Bechara Zainúm, Montería, Córdoba 230001, Colombia. yafaheju@hotmail.com.
Diagnostics (Basel, Switzerland)
|May 12, 2019
Summary
This study introduces a data-driven fuzzy clinical decision support system (CDSS) methodology. The developed system achieved high accuracy in disease diagnosis, outperforming existing literature methods.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
Background:
- Clinical decision support systems (CDSS) aid in disease diagnosis.
- Fuzzy inference systems are a component of CDSS.
Purpose of the Study:
- To design, implement, and validate a methodology for data-driven Mamdani-type fuzzy CDSS.
- Utilize clustering and pivot tables for system development.
Main Methods:
- Developed a novel methodology for fuzzy CDSS.
- Applied algorithms to five public datasets: Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section.
- Compared results with existing literature.
Main Results:
- Achieved Kappa Statistics and accuracies close to 1.0% and 100%, respectively.
- Demonstrated superior accuracy compared to some literature results.
- The framework exhibits characteristics of deep learning due to multi-level data abstraction.
Conclusions:
- The proposed methodology effectively develops accurate fuzzy CDSS.
- The system shows promise for improving clinical decision-making.
- The approach aligns with deep learning principles for data representation.
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