Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset.

Decision Support Systems (DSSs) are solutions that serve decision-makers in their decision-making process. For the development of these intelligent systems, two primary components are needed: the knowledge database and the knowledge rule base. The objective of this research work was to implement and validate diverse clinical decision support systems supported by Mamdani-type fuzzy set theory using clustering and dynamic tables.

Cryopreservation of the Microalgae sp.

Each phytoplankton species presents a different behavior and tolerance to the cryopreservation process. Therefore, in a species-specific protocol, it is essential to ensure both growth and post-thawing cell viability. In this study, we explored the effect of cryopreservation of sp.

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

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.