Algorithm 1 Summary of the methodology using ML techniques to predict cardiac patients.

1:Collect datasets with the response variable, the presence of heart disease Y, and explanatory variables $V_j$, with $j\in \{1,\cdots ,r\}$. 2:Choose indicators that may be relevant in the classification of people with heart disease, obtained from the variables in Step 1, as CS indicators. 3:Perform a data analysis of the indicators defined in Step 2 and conduct tests of differences between medians. 4:Define a measure of consistency to differentiate false signatures of subjects in a dataset as the adapted consistency measure. 5:Use techniques to select the relevant explanatory variables in the prediction of heart disease, such as InfoGain, VIF, ANOVA, AIC, ANOVA + VIF, or AIC + VIF. 6:Formulate ML classification models, such as Adaboost, LR, NB, RF, and SVR. 7:Apply the ML models from Step 6 to classify people with heart disease. 8:Select the best ML model using performance measures, considering higher mean values of accuracy, sensitivity, specificity, and ATPP.