The objective of this study was to design, implement, and validate different intelligent fuzzy clinical decision support systems based on a fuzzy set theory using clusters and pivot tables. The results were compared with other related works (Literature) to validate the proposed fuzzy systems for classifying the Coimbra breast cancer dataset. The validation methods used were cross-validation and random sampling for each comparison. The fuzzy Inference Systems had different input variables according to the ones mentioned in the literature. The originality of this work lies in the way of generating the membership functions and the rule base for the intelligent fuzzy clinical decision support systems. The results show that the Kappa Statistics and accuracy in some cases were higher than the obtained results from the literature for the output variable for the different Fuzzy Inference Systems - FIS, showing better accuracy. In a significant conclusion, these outcomes offer favorable evidence that models combining features such as age, BMI, and metabolic parameters can be an effective tool for a low-cosvaluableuseful biomarker for the Coimbra breast cancer dataset.