Acute Coronary Syndrome Prediction Using Data Mining Techniques- An Application
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.
[1] American Heart Association - acute coronary syndrome. Available:
http://www.americanheart.org.
[2] M. A. Chisholm-Burns, B. G. Wells, T. L. Schwinghammer, P. M.
Malone, J. M. Kolesar, J. C. Rotschafer and J. T. Dipiro,
Pharmacotherapy Principles & Practice, McGraw Hill 2007, chapter
5.
[3] M. I. Danish, Medical Diagnosis and Management, 5th edition.
[4] S.A. Spinler, Pharmacotherapy Self-Assessment Program-acute
coronary syndrome, 5th Edition.
[5] S. F. Kazim, A. Itrat, N. W. Butt and M. Ishaq, "Comparison of
cardiovascular disease patterns in two data sets of patients admitted at
a Tertiary Care Public Hospital in Karachi five years apart”, Pak J
Med Sci 2009, vol. 25, no.1, pp. 55-60.
[6] N. Lavesson, A. Halling, M. Freitag, J. Odeberg, H. Odeberg, P.
Davidsson (2009), "Classifying the severity of an Acute Coronary
Syndrome by Mining Patient Data”, 25th Annual Workshop of the
Swedish Artificial Intelligence Society, Linköping University
Electronic Press, ISSN 1650-3686.
[7] C. L. McCullough, A. J. Novobilski, F. M. Fesmire (2007), "Use of
Neural Networks to Predict Adverse Outcomes from Acute Coronary
Syndrome for Male and Female Patients”, 6th International
Conference on Machine Learning and Applications (ICMLA), 13-15,
December. Cincinnati, Ohio, USA.
[8] H. Kostakis , B. Boutsinas, D. B. Panagiotakos and L. D. Kounis
(2008), "A Computational Algorithm for the Risk Assessment of
Developing Acute Coronary Syndromes, Using Online Analytical
Process Methodology Source”, International Journal of Knowledge
Engineering and Soft Data Paradigms, Pages 85-99.
[9] R. B. Rao, S. Krishnan and R. S. Niculescu (2006), Data mining for
Improved Cardiac Care, ACM SIGKDD Explorations Newsletter,
8(1), pp. 3 – 10.
[10] I. A. Scott, C. P. Denaro, J. L. Flores, C. J. Bennett, A. C. Hickey and
A. M. Mudge (2002), Quality of care of patients hospitalized with
acute coronary syndromes, Royal Australasian College of Physicians,
Australia.
[11] Massad E., Ortega N. R.S., L. C Barros and C. J. Struchiner (2008),
"...and Beyond: Fuzzy Logic in Medical Diagnosis”, Fuzzy Logic in
Action: Applications in Epidemiology and Beyond, Studies in
Fuzziness and Soft Computing, vol, 232/2008. Springer-Verlag.
[12] E. B. M. Tamil, N. H. Kamarudin, R. Salleh and A. M. Tamil(2008),
A Review on Feature Extraction & Classification Techniques for
Biosignal Processing (Part I: Electrocardiogram), 4th Kuala Lumpur
International Conference on Biomedical Engineering (BIOMED), 25–
28 June 2008 Kuala Lumpur, Malaysia, pp. 107-112. IFMBE
Proceedings, Springer-Verlag
[13] A. Quteishat , C. P. Lim(2008), "Application of the Fuzzy Min-Max
Neural Networks to Medical Diagnosis”, Lecture Notes In Artificial
Intelligence, vol. 5179.
[14] Proceedings of the 12th international conference on Knowledge-
Based Intelligent Information and Engineering Systems, Part III, pp.
548 – 555, Springer-Verlag
[15] M. H. Dunham and S. Sridhar, Data Mining: Introductory and
Advanced topics, Pearson Education 2006, chapter 1, chapter 3,
chapter 4.
[16] M. Kantardzic, Data Mining: Concepts, Models, Methods, and
Algorithms. John Wiley & Sons 2003, chapter 5.
[17] D. T. Larose, Data mining methods and models. John Wiley and sons,
2006, chapter 4.
[1] American Heart Association - acute coronary syndrome. Available:
http://www.americanheart.org.
[2] M. A. Chisholm-Burns, B. G. Wells, T. L. Schwinghammer, P. M.
Malone, J. M. Kolesar, J. C. Rotschafer and J. T. Dipiro,
Pharmacotherapy Principles & Practice, McGraw Hill 2007, chapter
5.
[3] M. I. Danish, Medical Diagnosis and Management, 5th edition.
[4] S.A. Spinler, Pharmacotherapy Self-Assessment Program-acute
coronary syndrome, 5th Edition.
[5] S. F. Kazim, A. Itrat, N. W. Butt and M. Ishaq, "Comparison of
cardiovascular disease patterns in two data sets of patients admitted at
a Tertiary Care Public Hospital in Karachi five years apart”, Pak J
Med Sci 2009, vol. 25, no.1, pp. 55-60.
[6] N. Lavesson, A. Halling, M. Freitag, J. Odeberg, H. Odeberg, P.
Davidsson (2009), "Classifying the severity of an Acute Coronary
Syndrome by Mining Patient Data”, 25th Annual Workshop of the
Swedish Artificial Intelligence Society, Linköping University
Electronic Press, ISSN 1650-3686.
[7] C. L. McCullough, A. J. Novobilski, F. M. Fesmire (2007), "Use of
Neural Networks to Predict Adverse Outcomes from Acute Coronary
Syndrome for Male and Female Patients”, 6th International
Conference on Machine Learning and Applications (ICMLA), 13-15,
December. Cincinnati, Ohio, USA.
[8] H. Kostakis , B. Boutsinas, D. B. Panagiotakos and L. D. Kounis
(2008), "A Computational Algorithm for the Risk Assessment of
Developing Acute Coronary Syndromes, Using Online Analytical
Process Methodology Source”, International Journal of Knowledge
Engineering and Soft Data Paradigms, Pages 85-99.
[9] R. B. Rao, S. Krishnan and R. S. Niculescu (2006), Data mining for
Improved Cardiac Care, ACM SIGKDD Explorations Newsletter,
8(1), pp. 3 – 10.
[10] I. A. Scott, C. P. Denaro, J. L. Flores, C. J. Bennett, A. C. Hickey and
A. M. Mudge (2002), Quality of care of patients hospitalized with
acute coronary syndromes, Royal Australasian College of Physicians,
Australia.
[11] Massad E., Ortega N. R.S., L. C Barros and C. J. Struchiner (2008),
"...and Beyond: Fuzzy Logic in Medical Diagnosis”, Fuzzy Logic in
Action: Applications in Epidemiology and Beyond, Studies in
Fuzziness and Soft Computing, vol, 232/2008. Springer-Verlag.
[12] E. B. M. Tamil, N. H. Kamarudin, R. Salleh and A. M. Tamil(2008),
A Review on Feature Extraction & Classification Techniques for
Biosignal Processing (Part I: Electrocardiogram), 4th Kuala Lumpur
International Conference on Biomedical Engineering (BIOMED), 25–
28 June 2008 Kuala Lumpur, Malaysia, pp. 107-112. IFMBE
Proceedings, Springer-Verlag
[13] A. Quteishat , C. P. Lim(2008), "Application of the Fuzzy Min-Max
Neural Networks to Medical Diagnosis”, Lecture Notes In Artificial
Intelligence, vol. 5179.
[14] Proceedings of the 12th international conference on Knowledge-
Based Intelligent Information and Engineering Systems, Part III, pp.
548 – 555, Springer-Verlag
[15] M. H. Dunham and S. Sridhar, Data Mining: Introductory and
Advanced topics, Pearson Education 2006, chapter 1, chapter 3,
chapter 4.
[16] M. Kantardzic, Data Mining: Concepts, Models, Methods, and
Algorithms. John Wiley & Sons 2003, chapter 5.
[17] D. T. Larose, Data mining methods and models. John Wiley and sons,
2006, chapter 4.
@article{"International Journal of Information, Control and Computer Sciences:54520", author = "Tahseen A. Jilani and Huda Yasin and Madiha Yasin and C. Ardil", title = "Acute Coronary Syndrome Prediction Using Data Mining Techniques- An Application", 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.
", keywords = "Acute coronary syndrome (ACS), binary logistic
regression analyses, myocardial ischemia (MI), principle component
analysis, unstable angina (U.A.).", volume = "7", number = "1", pages = "63-5", }
