# Choose the feature that maximizes the evaluation function J()
## 3 Results and Discussion
### Correlation and Performance of the Non-Invasive Attributes
Given that, by the essence of cardiovascular diseases, elevated glucose and cholesterol are highly correlated with the development of these diseases. However, this study seeks to generate a classification based on non-invasive attributes, given the conditions of many health clinics in Mexico. Where blood studies usually take several days or even weeks. It is important to note that this preliminary study seeks to discuss this alternative for the use of artificial intelligence and the implementation of a new stage in the implemented methodology, in addition to the requirements and disadvantages of measuring cholesterol and glucose non-invasively [26]. However, the attributes \"angina\", \"frequency\", and \"chest pain\" are the ones that will have the highest statistical correlation in the classification. Thus, these eight attributes were tested in the following stage. Furthermore, the forward selection process (see Figure 11) revealed that the predictive performance stabilizes after selecting five attributes. The AUC shows a consistent trend across five, six, seven, and eight attributes, indicating that adding more features does not significantly improve classification performance. This suggests that an optimal balance between model accuracy and computational efficiency can be achieved with a reduced set of attributes, avoiding unnecessary complexity while maintaining high predictive capability. Figure 10: Heat map of the correlation coefficients of the 8 attributes assigned as “non-invasive”. The forward selection analysis highlights that, after selecting five attributes, the model's performance remains stable, making additional features redundant. This reduction in the number of attributes leads to a 50% decrease in computational cost and reduces the workload for medical professionals, improving efficiency in the classification process. Based on this analysis, the five key attributes that achieve similar classification performance as the full eight-attribute model are sex, age, chest pain, heart rate, angina, and condition. The stepwise addition of attributes follows this sequence:  
1. Condition, Heart Rate
2. Condition, Chest Pain, Heart Rate
3. Condition, Chest Pain, Heart Rate, Exercise-Induced Angina
4. Condition, Chest Pain, Heart Rate, Angina, Sex
5. Condition, Chest Pain, Heart Rate, Angina, Sex, Age
6. Condition, Chest Pain, Heart Rate, Angina, Sex, Age, Cholesterol
7. Condition, Chest Pain, Heart Rate, Angina, Sex, Age, Cholesterol, Blood Pressure
8. Condition, Chest Pain, Heart Rate, Angina, Sex, Age, Cholesterol, Blood Pressure, Sugar  
A key finding of this study is that an accurate prediction of high or low cardiovascular disease risk can be obtained using just one measurement instrument and a few patient-reported attributes. The best achieved accuracy is 87%, meaning that 87 out of every 100 patients are correctly classified, with a 13% margin of error. This demonstrates the effectiveness of a simplified approach, ensuring reliable results while minimizing the need for costly and invasive procedures. The difference in the area under the curve (AUC) between using five and eight attributes is approximately 0.05. While this change is negligible in terms of performance improvement, it represents a significant reduction in the number of attributes required, optimizing efficiency without compromising accuracy. Figure 11: Forward selection performance.