Key result
Data mining models isolate 14 key clinical predictors for acute coronary syndrome risk.
Why the study?
Factors that contribute significantly to enhancing the risk of acute coronary syndrome need to be investigated using data mining techniques for better prediction.
Observational
Yes
Data mining techniques such as binary regression and principal component analysis can be applied to clinical variables to predict the presence of acute coronary syndrome.
Supports exploratory ACS prediction modeling with PCA; hypothesis-generating and requires prospective validation.
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.
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Jilani et al. (2009) conducted an observational in Acute coronary syndrome. Clinical risk factors was evaluated on Diagnosis of acute coronary syndrome. Data mining techniques using binary regression and principal component analysis identified 14 clinical factors for predicting the risk of acute coronary syndrome.
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