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Research Article Open Access
In recent decades, heart disease has been identified as the leading cause of death across the world. However, it is considered as the most preventable and controllable disease at the same time. According to World Health Organization (WHO), the early and timely diagnosis of heart disease plays a remarkable role in preventing its progress and reducing related treatment costs. Considering the ever-increasing growth of heart disease-induced fatalities, researchers have adopted different data mining techniques to diagnose it. According to results, application of the same data mining techniques leads to different results in different datasets. This study tries to assist healthcare specialists to early diagnose heart disease and assess related risk factors. To this end, the main heart disease diagnosis indices were identified using experts’ opinions. Then, data mining techniques were applied on a heartrelated dataset. Finally, the main heart disease diagnosis indices were identified and a model was developed based on extracted rules. Visual Studio was used to write the algorithm code.
Bayesian network, Data mining, Decision tree, Heart disease, K-nearest neighbor, Support vector machines, Economic Capital, Financial Economics, Hospitality Management, Industrial and Management Optimization, Innovation Policy and the Economy, Socio-Economic Planning Sciences, Economic indicator, Total Quality Management (TQM), Value based Management, Entrepreneurial Development, Management in Education, Classical Economics, Monetary Neutrality, Econometrics, New Economy, Welfare Economics, Development Economics, Economic Transparency, Globalisation, Game theory