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Mahmoodi M S. Designing a Heart Disease prediction System using Support Vector Machine. jhbmi 2017; 4 (1) :1-10
URL: http://jhbmi.ir/article-1-182-en.html
URL: http://jhbmi.ir/article-1-182-en.html
M.Sc. Computer Sciences, Computer Engineering Dept., Birjand Payame Noor University (PNU), Birjand, Iran.
Abstract: (8641 Views)
Introduction: Cardiovascular diseases are the leading cause of death worldwide. The world health
Organization has estimated 12 million deaths per year worldwide, due to the cardiovascular diseases. The main aim of this study was to design a smart computer-aided system for the diagnosis of heart disease in patients.
Methods: In this study descriptive and analytical study, data of 270 people with 13 features were used. The fuzzy system and support vector machine classifier were combined using facilities of MATLAB software and were simulated by a system of core i5 and windows7, as the operating system, to diagnose patients with heart disease.
Results: Fuzzy technique and support vector machine algorithm that were used for the diagnosis of heart disease was efficient in rapid diagnosis and consequently increasing the patient chance for survival. Evaluation criteria in this system were rates of categorization and sensitivity and the system performance for these two indicators were respectively 85% and 85.8%.
Conclusion: According to the results, the proposed system can diagnose patients with cardiovascular diseases with a relatively high accuracy.
Organization has estimated 12 million deaths per year worldwide, due to the cardiovascular diseases. The main aim of this study was to design a smart computer-aided system for the diagnosis of heart disease in patients.
Methods: In this study descriptive and analytical study, data of 270 people with 13 features were used. The fuzzy system and support vector machine classifier were combined using facilities of MATLAB software and were simulated by a system of core i5 and windows7, as the operating system, to diagnose patients with heart disease.
Results: Fuzzy technique and support vector machine algorithm that were used for the diagnosis of heart disease was efficient in rapid diagnosis and consequently increasing the patient chance for survival. Evaluation criteria in this system were rates of categorization and sensitivity and the system performance for these two indicators were respectively 85% and 85.8%.
Conclusion: According to the results, the proposed system can diagnose patients with cardiovascular diseases with a relatively high accuracy.
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