Excellence in Research and Innovation for Humanity
@article{(International Science Index):http://waset.org/publications/5423,
  title    = {Acute Coronary Syndrome Prediction Using Data Mining Techniques- An Application},
  author    = {Tahseen A. Jilani and  Huda Yasin and  Madiha Yasin and  Cemal Ardil},
  country   = {},
  institution={},
  abstract  = {In this paper we use data mining techniques to investigate factors that contribute significantly to enhancing the risk of acute coronary syndrome. We assume that the dependent variable is diagnosis – with dichotomous values showing presence or  absence of disease. We have applied binary regression to the factors affecting the dependent variable. The data set has been taken from two different cardiac hospitals of Karachi, Pakistan. We have total sixteen variables out of which one is assumed dependent and other 15 are independent variables. For better performance of the regression model in predicting acute coronary syndrome, data reduction techniques like principle component analysis is applied. Based on results of data reduction, we have considered only 14 out of sixteen factors.
},
    journal   = {International Journal of Computer, Electrical, Automation, Control and Information Engineering},  volume    = {7},
  number    = {1},
  year      = {2013},
  pages     = {168 - 172},
  ee        = {http://waset.org/publications/5423},
  url       = {http://waset.org/Publications?p=73},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 73, 2013},
}