| Paper title: |
Using Machine Learning Algorithms in Cardiovascular Disease Risk Evaluation |
| Published in: | Issue 1, (Vol. 3) / 2009 |
| Publishing date: | 2009-04-14 |
| Pages: | 29-32 |
| Author(s): | SITAR - Taut V. A., ZDRENGHEA D., Pop D., SITAR - Taut A. D. |
| Abstract. | Even if Medicine and Computer Science seem apparently intangible domains, they collaborate each other for few decades. One of the faces of this cooperation is Data Mining, a relative new and multidisciplinary field capable to extract valuable information from large sets of data. Despite this fact, in cardiology related studies it was rarely used. We assume that some data mining tools can be used as a substitute for some complex, expensive, uncomfortable, time consuming, and sometimes dangerous medical examinations. This paper aims to show that cardiovascular diseases may be predicted by classical risk factors analyzed and processed in a “non-invasive” way. |
| Keywords: | Cardiovascular Disease, Machine Learning Algorithms |
| References: | 1. D.-A. Sitar-Taut, “Baze de date distribuite”, Risoprint Publishing House, pp291-295, 2005. 2. V.P. Bresfelean, “Implicaţii ale tehnologiilor informatice asupra managementului instituţiilor universitare”, Risoprint Publishing House, pp 127-170, 2008. 3. P. Andreeva, “Data Modelling and Specific Rule Generation via Data Mining Techniques”, International Conference on Computer Systems and Technologies - CompSysTech’ 2006. 4. P. Herron, “Machine Learning for Medical Decision Support: Evaluating Diagnostic Performance of Machine Learning Classification Algorithms”, Data Mining. Spring 2004. 5. L. Zhang, W.M. Kim Roddis, “Machine Learning in Updating Predictive Models of Planning and Scheduling Transportation Projects”, Transportation Research Record, TRB, No. 1588, pp. 86-94, 1997 . 6. I.-N. Lee, S.-C. Liao and M. Embrechts, “Data mining techniques applied to medical information” . Med. inform. vol. 25, no. 2, pp81- 102, 2000. 7. http://www.cs.kuleuven.ac.be/~dtai/ml/research 8. I. Harris, J. Denzinger and D. Yergens,“Application of the Weka Machine Learning Library to Hospital Ward Occupancy Problems”, Canada Technical Report 2007fi884fi36. December 12, 2007. 9. http://en.wikipedia.org/wiki/Machine_learning 10. N.J. Nilsson, “Introduction to Machine Learning”, http://www.wepapers.com/Papers/12236/INTRODUCTION_to_ machine_learning 11. I.H. Witten and E. Frank, ”Data Mining: Practical machine learning tools and techniques”, 2nd Edition, Morgan Kaufmann, San Francisco, 2005 12. http://en.wikipedia.org/wiki/Decision_Tree 13. L. Mosca, C.L Banka, E.J. Benjamin, K. Berra, C. Bushnell, R.J. Dolor et al, for the Expert Panel/Writing Group,”EvidenceBased Guidelines for Cardiovascular Disease Prevention in Women: 2007 Update” JACC (49):1230–1250, 2007. 14. F. Alfonso, J. Bermejo, J. Segovia, “Cardiovascular disease in women. Why now?” Review, Rev Esp Cardiol; 59(3):259-263. March 2006. 15. M. Stramba-Badiale, K.M. Fox, S.G. Priori, P. Collins, C. Daly, I. Graham, et al, “Cardiovascular diseases in women: a statement from the policy conference of the European Society of Cardiology”, Eur Heart J (27); 994-1005, 2006. 16. N.K. Wenger, L.J. Shaw, V. Vaccarino, “Coronary heart disease in women: update 2008”, Clin Pharmacol Ther; 83 (1):37-51, 2008. 17. L. Pilote, K. Dasgupta, V. Guru, K. Humphries, J. McGrath et al. „A comprehensive view of sex-specific issues related to cardiovascular disease”, CMAJ; 176(6) S1-41, 2007. 18. M.A. Turkman, “Predictive tools in the assessment of diagnostic tests”, Third Workshop on Statistics, Mathematics and Computation First Portuguese-Polish Workshop on Biometry. Lisbon, 21-22 July Universidade Aberta – PORTUGAL, 2008. www.univ-ab.pt/wemc2008 |
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