## 3 Results and Discussion
The accuracy_score(y_test, clf_rf.predict(x_test)) = 0.0.96 means that the model correctly predicted the target variable for 96% of the samples in the test data, and accuracy_score (y_train, clf_rf.predict(x_train)) = 0.99 means that the model correctly predicted the target variable for 99% of the samples in the training data. The prediction was also conducted with random forest models, and we obtained the same prediction (Figures 9 and 10). In the context of a health problem prediction project, the accuracy_score function is used to evaluate the performance of neural networks comprising random forest. The results revealed that both models performed very well with high accuracy scores. The accuracy_score for random forest on the test dataset was 0.96, indicating its ability to predict health problems with a high level of accuracy. It was expected that the random forest model would show high performance because of small data. The neural networks model also achieved a high accuracy score of 0.97, demonstrating high performance in predicting health problems. These results suggest that both models can effectively predict health problems; however, the neural networks model outperformed in terms of accuracy compared with other research references and considering the complexity of overfitting. This is certainly a significant improvement over logistic regression classifiers, which range between 0.903 and 0.917 in the prediction of comorbidity (more than one chronic disease), investigated by [PERSON] and [PERSON] (2018). Comparative analysis of the results is essential to evaluate the effectiveness of our proposed obesity prediction system. Studies directly related to our work are limited in number. We identified some relevant studies in the areas of childhood obesity risk prediction, diabetic risk prediction from obesity, and disease prediction. Despite the scarcity of comparable studies, we made efforts to compare our work with others based on specific parameters. [PERSON] et al. (2018) used random forest to recognize 19 types of physical activities with 93.4% accuracy, although the limited number of subjects may impact generalizability. [PERSON] et al. (2018) employed random forest to explore factors affecting body weight, reporting an estimated error rate of 40% and highlighting the algorithm's ability to handle highly correlated variables; however, the accuracy of the model was affected by the low number of correlated variables used in the study. In the study of [PERSON] et al. (2018), if we  
Figure 2:2 Prediction of the outcome for an individual (0—female) at age 55, with BMI of 24 (kg/m\({}^{2}\)), glucose level of 5 (mmol/L), and cholesterol level of 5 (mmol/L). An outcome predicted as 0 (array [0]) means healthy person/normal weight. compare the results regarding accuracy--k-NN, 77.5%; random forest, 72.3%; and logistic regression, 97,09%--we can see that the accuracy of our developed neural network model is very close to the accuracy of logistic regression, which represents a significant advance in nutrition and neural network applications. It must be emphasized that this high accuracy was achieved due to the nature of the data. The dataset has 400 points. All 200 patients who were overweight and had some health problem, under the keto diet regime, had rich biochemical results regulated as well as their weight. The data training was in 50 iterations; so, a very well-trained model was achieved.