## 2 Related Work  
In this section, we present selected applications of ontologies for effective knowledge modeling and information retrieval in diagnosing diseases. In [21], the authors proposed a method to convert an ontology into a database using predefined transformation rules. We discuss the application of ontologies for representing knowledge within relational databases, as well as its potential for use in web application development.  
In addition to known computational algorithms, there is also an alternative approach to predict CVD risk, which involves the use of risk prediction models. The World Health Organization/International Society of Hypertension (WHO/ISH) risk score charts are widely used models to predict CVD risk [22]. These graphs are accompanied by practice notes that provide guidance on interpreting and adjusting scores, as well as on managing risk factors that are not included in the risk score calculations.  
A recent study by [23] evaluated the use of WHO/ISH risk prediction charts to assess cardiovascular risk in three low- and middle-income countries in Asia. The study found that these graphs have certain advantages. One limitation is that they may underestimate the risk of CVD if individuals already on treatment are not taken into account. The degree of underestimation varies depending on the extent of coverage and the effectiveness of drug treatment.  
The Framingham Risk Score (FRS) is another widely used model to calculate CVD risk. A study by [24] investigated the performance of this model by conducting a validation study on 959 randomly selected individuals aged 30-74 years. The results of the experiment showed that the model tended to overestimate the risk in older women and middle-aged men. On the basis of these findings, the researchers suggest that the model should be reclassified to account for these slight errors.  
The systematic coronary risk evaluation (SCORE) model, which is used to predict the risk of the first fatal atherosclerotic cardiovascular event in 10 years, takes into account factors such as age, gender, smoking, total cholesterol, and systolic blood pressure (SBP). In [25], [PERSON] et al. examined the effectiveness of this model in different regions of Europe with varying levels of CVD risk. The study proposed improvements to the model, but it should be noted that the results may not fully reflect an individual's typical risk due to the nature of the study being conducted in a single instance.  
[PERSON] et al. [26] conducted an evaluation of the FRS, SCORE, and WHO/International Society of Hypertension (WHO/ISH) models. The results showed that the WHO/ISH model was inadequate for the stratification of cardiovascular risk.  
However, the Framingham and SCORE models successfully identified the risk levels of Asian men and women. In particular, the SCORE high model accurately predicted the risk for men, but not for women. On the other hand, the Framingham model improved the detection of women's risk compared with the SCORE model. In general, it was found that the Framingham and SCORE models were more effective at identifying cardiovascular risk in Asian men and women than the WHO/ISH labels [27; 28; 29].  
In [8], [PERSON] et al. compared the performance of random Forest, logistic regression, decision tree, Naive Bayes, k-Nearest Neighbors, artificial neural network, and support vector machine with ontology. The results show that ontology model achieved a higher accuracy (0.755) than the other examined models (0.731 for decision tree, 0.721 for logistic regression, 0.715 for random forest, 0.648 for support vector machines, 0.645 for artificial neural networks, 0.59 for Naive Bayes, and 0.571 for k-nearest neighbors).