Journal
NEURAL COMPUTING & APPLICATIONS
Volume 29, Issue 10, Pages 685-693Publisher
SPRINGER
DOI: 10.1007/s00521-016-2604-1
Keywords
Machine learning; Classification; Heart disease; Treatments; Artificial neural network; Support vector machine
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Heart diseases are of notable public health disquiet worldwide. Heart patients are growing speedily owing to deficient health awareness and bad consumption lifestyles. Therefore, it is essential to have a framework that can effectually recognize the prevalence of heart disease in thousands of samples instantaneously. At this juncture, the potential of six machine learning techniques was evaluated for prediction of heart disease. The recital of these methods was assessed on eight diverse classification performance indices. In addition, these methods were assessed on receiver operative characteristic curve. The highest classification accuracy of 85 % was reported using logistic regression with sensitivity and specificity of 89 and 81 %, respectively.
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