## 3 Results and Discussion
In the context of the health problem prediction and BMI, precision, recall, and F1-score are used to evaluate how well the models are performing at identifying patients with and without health problems. The accuracy_score() function from scikit-learn is used to calculate the accuracy score of 0.98 of the categorical model on the test dataset, which is the percentage of correctly classified samples, which means 98% of the samples in the test dataset were correctly classified by the model. Such accuracy is common even when neuro-fuzzy classifiers are used [45; 46]. The macro average and weighted average for precision, recall, and F1-score are also reported. The macro average calculates the average performance across all classes, while the weighted average considers the number of samples in each class. Overall, the model has a high level of performance, with precision 0.96, macro and weighted averages 0.98, recall 0.98, and F1-score above 0.98. The next step in the context of health problem prediction and BMI is to perform prediction tests of some individual person with specific health parameters and their own BMI. Several tests were conducted with normal and elevated levels of glucose in the blood along with cholesterol parameters (Figure 9). In the context of the health problem prediction and BMI, precision, recall, and F1-score are used to evaluate how well the models are performing at identifying patients with and without health problems. The accuracy_score() function from scikit-learn is used to calculate the accuracy score of 0.98 of the categorical model on the test dataset, which is the percentage of correctly classified samples, which means 98% of the samples in the test dataset were correctly classified by the model. Such accuracy is common even when neuro-fuzzy classifiers are used [45; 46]. The macro average and weighted average for precision, recall, and F1-score are also reported. The macro average calculates the average performance across all classes, while the weighted average considers the number of samples in each class. Overall, the model has a high level of performance, with precision 0.96, macro and weighted averages 0.98, recall 0.98, and F1-score above 0.98. The next step in the context of health problem prediction and BMI is to perform prediction tests of some individual person with specific health parameters and their own BMI. Several tests were conducted with normal and elevated levels of glucose in the blood along with cholesterol parameters (Figure 9). Figure 8: The classification_report and accuracy_score, using predictions: (**a**) categorical model; (**b**) binary model. Figure 2:0 Model performing on the on the training data for binary classification problem. Figure 2:1 Prediction of the outcome for an individual (1—male) at age 35, with BMI of 30.7 (kg/m\({}^{2}\)), glucose level of 6 (mmol/L), and cholesterol level of 6 (mmol/L). An outcome predicted as 1 (array [1]) means a person with health problems and overweight. An output of 1 is obtained in the case when some of the parameters have an increased value--for example, glucose, cholesterol, triglycerides, or platelets--but also BMI (Figure 8), where an output of 0 (Figure 10) is obtained if the parameters are within the normal range and BMI. In the final step, the dataset of participants was also evaluated with random forest models, considering that random forest is a popular algorithm suitable for small data. Importing the RandomForestClassifier class from'sklearn.ensemble' implements the random forest algorithm for classification tasks and assigns it to the variable 'clf_rf'. Fitting the classifier to the training data, clf_rf.fit(x_train, y_train), the 'clf_rf' object will be a trained random forest classifier that can be used for making predictions on new data. The scikit-learn library is used to calculate the accuracy scores for the trained random forest classifier on both the training and test data.