@article{(Open Science Index):https://publications.waset.org/pdf/10002699,
	  title     = {Applying the Regression Technique for Prediction of the Acute Heart Attack},
	  author    = {Paria Soleimani and  Arezoo Neshati},
	  country	= {},
	  institution	= {},
	  abstract     = {Myocardial infarction is one of the leading causes of
death in the world. Some of these deaths occur even before the
patient reaches the hospital. Myocardial infarction occurs as a result
of impaired blood supply. Because the most of these deaths are due to
coronary artery disease, hence the awareness of the warning signs of
a heart attack is essential. Some heart attacks are sudden and intense,
but most of them start slowly, with mild pain or discomfort, then
early detection and successful treatment of these symptoms is vital to
save them. Therefore, importance and usefulness of a system
designing to assist physicians in early diagnosis of the acute heart
attacks is obvious. The main purpose of this study would be to enable patients to
become better informed about their condition and to encourage them
to seek professional care at an earlier stage in the appropriate
situations. For this purpose, the data were collected on 711 heart
patients in Iran hospitals. 28 attributes of clinical factors can be
reported by patients; were studied. Three logistic regression models
were made on the basis of the 28 features to predict the risk of heart
attacks. The best logistic regression model in terms of performance
had a C-index of 0.955 and with an accuracy of 94.9%. The variables,
severe chest pain, back pain, cold sweats, shortness of breath, nausea
and vomiting, were selected as the main features.
},
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {9},
	  number    = {11},
	  year      = {2015},
	  pages     = {767 - 771},
	  ee        = {https://publications.waset.org/pdf/10002699},
	  url   	= {https://publications.waset.org/vol/107},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 107, 2015},
	}