Kardı̇ovasküler hastalık oluşum mekanı̇zmalarını anlamak ı̇çı̇n verı̇ madencı̇lı̇ğı̇ yöntemlerı̇ ve makı̇ne öğrenmesı̇ modellerı̇nı̇n gelı̇ştı̇rı̇lmesı̇

World Health Organization (WHO) reported that in 2016, 31% (17.9 million) of the total deaths in the world were caused by Coronary Artery Disease (CAD) and it is estimated that around 23.6 million people will die from CAD in 2030. In the following years, this disease will cause millions of more deaths and the diagnosis and treatment will cost billions of dollars. CAD, which is a sub-category of Cardiovascular Disease (CVD), is the inability to feed the heart with blood as a result of the accumulation of fatty matter called atheroma on the walls of the arteries. With the development of machine learning and data mining techniques, it became possible to diagnose Cardiovascular Diseases (CVD), especially CADs, at a lower cost via checking some physical and biochemical values. To this end, in this thesis, for CVD diagnosis problem, different computational feature selection (FS) methods, dimension reduction, and different classification algorithms have been evaluated; and a domain knowledge-based FS method, an ensemble FS method and a probabilistic FS method have been proposed. Via experimenting on two publicly available data sets, i.e., UCI Cleveland and Z- Alizadehsani, this thesis aims to generate a robust model for the diagnosis of CVD, at a lower cost. In our experiments, our proposed solution achieved 91.78% accuracy and 93.50% sensitivity on the diagnostic tests.