Table 6.

Pivot tables substages and decision support systems’ implementation steps.

DDMTFCDSS Activity Steps	Activity Description
9.1 Combining different input variable cluster datasets.	The objective in this stage is to make combinations between input variables clusters datasets generated in the previous step, to find the best performance. For doing this, we can use the command nchoosek from any matrices laboratory software—Appendix C.
9.2 Establishing the fuzzy rules.	This section is based on the previous one. If the operations carried out with the use of the pivot tables find one or several combinations that guarantee good results (not clusters overlapping or minimum differences between the values), it proceeds to make the rules base of the fuzzy system. This is done by the recommendations of the previous section (using the command Unique). The rules will be easily detectable. To see an example refers to the case studies—Appendix C.
Data Driven Mamdani-Type Fuzzy Clinical Decision Support System
10. Elaborating the Decision Support System based on a fuzzy set theory (Inference engine).	This stage refers to the implementation in specific software for the elaboration of fuzzy model systems such as Matlab® [83], Xfuzzy® [84], sciFLT® [85], and more. For doing this, until the moment, we must have, through the current framework, the following elements: definition of the linguistic variables, the rules set, the sets number, and the membership function values of each variable. In this step, the modeler must know the environment of the software platform to work with each of the elements previously mentioned. The recommendation for this stage is that the modeler may need to manually adjust the values of the sets suggested by the methodology, until achieving the desired values (Appendix F).
11. Evaluating the fuzzy inference system performance (defuzzification and Crisp Outputs).	This stage aims to measure the designed and implemented system performance until this moment. For doing this, we must use the evaluation functions of each specific program and realize the simulations with the observed data and with the mean values of these or the test data subset (Appendix E). In this case, the recommendation is to try to perform the simulation and get the results in a table format, where you can realize some statistical calculations that allow us to evaluate the model’s performance. As part of the recommendation, for regression problems, within the calculations carried out by the system for each output variable, there are the following statistical indexes: absolute deviation, standard deviation, percentage error, a graph of coefficient of Correlation R, and a calculation of the coefficient of determination R2 (see Appendix E and Appendix G). Other statistical values can be calculated. However, they could be made in a spreadsheet with the results of the output variables observed and those predicted by the system. These calculations can be: standard error, Root Mean Square error (RMSE), regression coefficients (slopes), intercepts, and more. If the obtained model’s results suggest that the system has a good or excellent performance, the modeling process is finished, changes are saved, and the results are shown. For classification problems, the system’s performance evaluation could be measured through the following metrics: The Classification accuracy (ACC), sensitivity, specificity, Function Measure, Area under the curve, and Kappa statistics. These evaluation metrics are explained in detail in Reference [26] and Reference [86] with their respective formulae.
End of Iterative Process
12. Communication.	This stage refers to the paper or documentation preparation. In this case, the modeler may show the results through a user manual or an academic and scientific journal article. If the main aim is to publish a journal article, the target population must be researchers or practitioners within the interest domain.