## 6 Discussion  
The use of an ontology-inspired database model and inner join or diagnosis demonstrates a reasonably good level of accuracy. Test 1 achieved a perfect accuracy rate of 100%, while Test 2 achieved a perfect accuracy rate of 100%, while Test 3 achieved a perfect accuracy rate of 100%.  
Figure 6: Diagnosis result panel. (1) Bar graph of disease-symptom matches; (2) The most probable diagnosis according to the app.  
Figure 7: Auto-generated report.  
yielded an accuracy of 75%. Taking the average of these two tests, we can conclude that the overall accuracy of the diagnosis process is 85%, without considering any bias.  
Other studies on the diagnosis of cardiovascular diseases based on ontologies reported an accuracy of 75.5% (10-fold cross-validation) in [8], sensitivity of 0.83 and precision of 0.87 in [7], and accuracy of more than 90% in [53].  
The accuracy of the decision trees, SVM, or ANN models proposed in [54] was 89.6%, 92.1%, and 91.0%, respectively. In our study, we achieved a sensitivity of 1.0 (100%), indicating that it performed excellently in detecting positive cases of cardiovascular disease. Furthermore, the combinations of diseases used to test both evaluated and existing applications were similar to those associated with cardiovascular disease.  
The reason for testing the application with real subjects diagnosed by physicians was to determine whether the application designed for the diagnosis of CVD can provide accurate diagnoses similar to those of physicians. However, only three patients with preexisting cardiovascular disease were tested, resulting in a small sample size that could introduce bias when evaluating the accuracy of the application.  
Furthermore, when diagnosing cardiovascular disease, doctors can also consider other coexisting conditions, as demonstrated in the first subject where the doctor's diagnosis included hypertension and diabetes mellitus. This highlights the complexity of the diagnosis of cardiovascular disease and the need to consider potential comorbidities.  
The second test aimed to compare the diagnosis provided by the application under evaluation with that of an established and proven application. On the basis of the results of the second test, several observations can be made. In general, when testing the application against the existing one, it exhibited an accuracy rate of 0.75 (75%) and a positive predictive value (PPV) of 0.714 (71.4%). This indicates that the application was 75% accurate in its diagnoses. This result was lower than reported in [7; 8; 53] and suggests the need for improvement by changing the sources and design of the ontology.  
However, the designed application has some limitations. First, the application was tested against the existing application only seven times. This limited sample size suggests a bias in the experiment and also implies that the accuracy values could be influenced by further testing. Second, only one architecture was considered because of the focus on the accuracy of diagnosis and the simplicity of the schema that implements the proposed ontology.  
Second, this application concentrates only on the main cardiovascular diseases and on diseases that closely resemble cardiovascular disease. As a result, we were working with approximately 14 tables using the ontology-based database model. The efficiency of the diagnosis process may vary in terms of speed, but this aspect was not thoroughly explored due to the limited sample size of the diseases under investigation.