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Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

1
Facultad de Ciencias Económicas, Administrativas y Contables, Universidad del Sinú Elías Bechara Zainúm, Montería, Córdoba 230001, Colombia
2
Departamento de Ciencias Acuícolas–Medicina Veterinaria y Zootecnia (CINPIC), Universidad de Córdoba, Montería, Córdoba 230001, Colombia
3
Facultad de Ingeniería, Departamento de Ingeniería de Sistemas, Universidad del Norte, Puerto Colombia, Atlántico 080001, Colombia
4
Facultad de Ingeniería, Departamento de Ingeniería de Sistemas, Universidad del Magdalena, Santa Marta, Magdalena 470001, Colombia
5
Facultad de Ingeniería, Universidad Francisco de Paula Santander, Cúcuta, Santander, 540001, Colombia
*
Author to whom correspondence should be addressed.
Diagnostics 2019, 9(2), 52; https://doi.org/10.3390/diagnostics9020052
Received: 20 March 2019 / Revised: 3 May 2019 / Accepted: 6 May 2019 / Published: 9 May 2019
(This article belongs to the Section Novel Diagnostic Technologies and Devices)
PDF [603 KB, uploaded 9 May 2019]

Abstract

Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.
Keywords: clusters; rule base; knowledge base; fuzzy sets; deep learning clusters; rule base; knowledge base; fuzzy sets; deep learning
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Hernández-Julio, Y.F.; Prieto-Guevara, M.J.; Nieto-Bernal, W.; Meriño-Fuentes, I.; Guerrero-Avendaño, A. Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems. Diagnostics 2019, 9, 52.

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