# Choose the feature that maximizes the evaluation function J()
## 3 Results and Discussion
### Correlation and Performance of the Non-Invasive Attributes  
Statistical analysis of numerical attributes (from the eight attributes assigned as non-invasive) provides key information on their influence for the classification of heart disease (Figure 8). Age shows a distribution slightly skewed to the right, with a range of 29 to 77 years and an average of 54 years. This indicates that risk increases with age, making it a relevant factor for classification. Resting blood pressure has a range of 94 to 200 mm Hg and an average of 131 mm Hg, with a distribution slightly skewed to the right, suggesting that, although hypertension is a risk factor, it is not a determinant predictor on its own. Although it is arguable that it may be a common condition for developing cardiovascular disease, in the case of this database, it may not prove to be relevant. Cholesterol varies between 126 and 564 mg/dL, with an average of 246 mg/dL, showing a distribution skewed to the right due to elevated values in some patients. As in the previous case, it would be evident that it is a determining factor; however, given the conditions of the database, it may be irrelevant. The maximum heart rate achieved has a range of 71 to 202 bpm and an average  
Figure 8: Histograms of the numerical attributes considered for the classification of cardiovascular disease. of 150 bpm, with a distribution skewed to the left, suggesting that patients with a lower heart rate response have a higher probability of presenting heart disease. On the other hand, for the categorical attributes, bar plots were obtained (Figure 9). Most patients present with asymptomatic pain, suggesting that the disease does not always manifest with typical symptoms, which reinforces the importance of assessing other attributes. Exercise-induced angina shows a strong relationship with cardiac disease, highlighting it as a key predictor in classification models. Patient sex indicates a male predominance in the database, reflecting the higher prevalence of cardiovascular disease in men, although this bias should be considered when training models to avoid distortions in prediction. The diagnostic variable (condition) has a balanced distribution, which favors adequate training of the models without bias toward a specific class. From a classification perspective, chest pain and exercise-induced angina are the most relevant indicators, while sex and diagnosis distribution provide context for the database. These findings reinforce the importance of combining categorical variables with non-invasive measurements, such as blood pressure and heart rate, to improve the accuracy of predictive models. From a machine learning perspective, these features provide crucial information for prediction. Age and heart rate stand out as the strongest indicators of the presence of disease, whereas blood pressure and cholesterol, although important in overall cardiovascular health, show less predictive capacity in this case. The use of non-invasive features such as these is fundamental to the practical implementation of classification models in clinical settings, as it allows for early detection without the need for costly or invasive laboratory tests. In addition, their immediate availability in medical consultations facilitates fast and efficient decision making. Considering the eight attributes classified as non-invasive, a heat map of the correlation matrix is presented to illustrate the relationships between them (see Figure 10). The analysis  
Figure 9: Histograms of the categorical attributes considered for the classification of cardiovascular disease. revealed that \"sugar\" and \"cholesterol\" exhibit a low correlation coefficient with respect to the 'condition' attribute. This suggests that these two attributes contribute minimally to the accurate classification of patients, and may not be essential for the model's predictive performance. These results may be questionable.