Objective: Cardiovascular Disease (CVD) is a disease that negatively affects the blood vessel system due to plaque formation as a result of accumulation on the inner wall of the vessels. In the diagnostic phase, angiography results are evaluated by physicians. New diagnostic algorithms based on artificial intelligence, including new technologies, are needed for diagnosing CVD due to the time-consuming and high cost of diagnostic methods. Materials and Methods: The heart disease dataset available on the open-source sharing site Kaggle was used in the study. The dataset includes 14 clinical findings. In the study, after the features were selected with the Fischer feature selection algorithm, they were classified with Ensemble Decision Trees (EDT), k-Nearest Neighborhood Algorithm (kNN), and Neural Networks (NN). A hybrid artificial intelligence algorithm was also created using the three methods. Results: According to the classification results, EDT %96.19, kNN %100, NN %86.17, and hybrid artificial intelligence determined CVD with a %99.3 success rate. Conclusion: According to the obtained results, it is evaluated that the proposed CVD diagnosis hybrid artificial intelligence algorithms can be used in practice

According to the World Health Organization (WHO) data, heart diseases are among the diseases with the highest mortality rate. Cardiovascular diseases, known as cardiovascular diseases, are defined as the formation of plaque on the inner wall of the vessel, the hardening of the vessels, the narrowing of the vessel and making the blood flow difficult. The diagnosis of the disease is made by examining various clinical findings. The clinical findings and tests take time, prolonging the diagnostic phase. For this reason, new tools and methods are being researched to facilitate the disease diagnosis process. Materials and Methods: Heart disease dataset from Kaggle, a public sharing site, was used in the study. There are 14 features in the dataset. The features were selected with the Eta correlation coefficient and reduced to 11. Rule-based diagnostic algorithms have been developed with the help of decision tree algorithms. Results: As a result of the study, rule-based algorithms were developed at approximately 5 levels, with an average accuracy rate of 94.15, sensitivity of 0.98, and specificity of 0.91. Conclusion: According to the model performances, it has a high accuracy rate developed with artificial intelligence methods for the diagnosis of CVD, and it is thought that it can be used as a rule-based diagnostic algorithm by the clinician.