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Journal of Medical Systems
Published in: Journal of Medical Systems 2/2013

01-04-2013 | Original Paper

Discovering Medical Knowledge using Association Rule Mining in Young Adults with Acute Myocardial Infarction

Authors: Dong Gyu Lee, Kwang Sun Ryu, Mohamed Bashir, Jang-Whan Bae, Keun Ho Ryu

Published in: Journal of Medical Systems | Issue 2/2013

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Abstract

The knowledge discovery has been widely applied to mine significant knowledge from medical data. Nevertheless, previous studies have produced large numbers of imprecise patterns. To reduce the number of imprecise patterns, we need an approach that can discover interesting patterns that connote causality between antecedent and consequence in a pattern. In this paper, we propose association rule mining method that can discover interesting patterns that include medical knowledge in Korean acute myocardial infarction registry that consists of 1,247 young adults collected by 51 participating hospitals since 2005. Proposed method can remove imprecise patterns and discover target patterns that include associations between blood factors and disease history. The association that blood factors affect to disease history is defined as target pattern. In our experiments, the interestingness of a target pattern is evaluated in terms of statistical measures such as lift, leverage, and conviction. We discover medical knowledge that glucose, smoking, triglyceride total cholesterol, and creatinine are associated with diabetes and hypertension in Korean young adults with acute myocardial infarction.
Literature
1.
Gu, D., Liang, C., and Li, X., Intelligent technique for knowledge reuse of dental medical records based on case-based reasoning. J. Med. Syst. 34:213–222, 2010.CrossRef
2.
Du, G., Jiang, Z., Diao, X., and Yao, Y., Knowledge extraction algorithm for variances handling of CP using integrated hybrid genetic double multi-group cooperative PSO and DPSO. J. Med. Syst. 36:979–994, 2012.CrossRef
3.
Arif, M., Malagore, I. A., and Afsar, F. A., Detection and localization of myocardial infarction using K-nearest neighbor classifier. J. Med. Syst. 36:279–289, 2012.CrossRef
4.
Shon, H. S., Ryu, K. S., Park, S.H., Bae, J.W., Cha, E. J. and Ryu, K. H., Risk factors of major adverse cardiac events after percutaneous coronary intervention in non ST elevation myocardial infarction. Int. Conf. Ubiquit. Healthc. 58–60, 2011.
5.
Li, P., Pok, G., Jung, K. S., Shon, H. S., and Ryu, K. H., QSE: A new 3-D solvent exposure measure for the analysis of protein structure. Proteomics 11(19):3794–3801, 2011.
6.
Bashir, M. E., Lee, D. G., Akasha, M., Yi, G. M., Cha, E. J., Bae, J. W., Cho, M. C., and Ryu, K. H., Highlighting the current issues with pride suggestions for improving the performance of real time cardiac health monitoring. Inf. Technol. Bio- Med. Inform 6266:226–33, 2010.
7.
Bashir, M. E., Ryu, K. S., Park, S. H., Lee, D. G., Shon, H. S., and Ryu, K. H., Superiority real-time cardiac arrhythmias detection using trigger learning method. Inf. Technol. Bio- Med. Informa. 6865:53–65, 2011.
8.
Shon, H. S., Ryu, K. H., Yang, K. S., and Yoo, C. W., Feature selection method using WF-LASSO for gene expression data analysis. ACM Conf. Bioinforma, Comput. Biol. Biomed. 522–24, 2011.
9.
Towbin, J. A., Bricker, J. T., and Garson, A., Electrocardiographic criteria for diagnosis of acute myocardial infarction in childhood. Am. J. Cardiol. 69(19):1545–1548, 1992.CrossRef
10.
Weinberger, I., Rotenberg, Z., Fuchs, J., Sagy, A., Friedmann, J., and Agmon, J., Myocardial infarction in young adults under 30 years: Risk factors and clinical course. Clin. Cardiol. 10(1):9–15, 1987.CrossRef
11.
Chouhan, L., Hajar, H. A., and Pomposiello, J. C., Comparison of thrombolytic therapy for acute myocardial infarction in patients aged <35 and >55 years. Am. J. Cardiol. 71(2):157–159, 1993.CrossRef
12.
Perski, A., Olsson, G., Landou, C., de Faire, U., Theorell, T., and Hamsten, A., Minimum heart rate and coronary atherosclerosis: Independent relations to global severity and rate of progression of angiographic lesions in men with myocardial infarction at a young age. Am. J. Cardiol. 123(3):609–616, 1992.
13.
AHA (American Heart Association), Heart and Stroke Facts Statistics. American Heart Association, Dallas, 1993.
14.
Kannel, W. B., and Abbott, R. D., Incidence and prognosis of unrecognized myocardial infarction. An update on the Framingham study. N. Engl. J. Med. 311(18):1144–1147, 1984.CrossRef
15.
Zimmerman, F. H., Cameron, A., Fisher, L. D., and Ng, G., Myocardial infarction in young adults: Angiographic characterization, risk factors and prognosis (Coronary Artery Surgery Study Registry). J. Am. Coll. Cardiol. 26(3):654–661, 1995.CrossRef
16.
Füllhaas, J. U., Rickenbacher, P., Pfisterer, M., and Ritz, R., Long-term prognosis of young patients after myocardial infarction in the thrombolytic era. Clin. Cardiol. 20(12):993–998, 1997.CrossRef
17.
Imazio, M., Bobbio, M., Bergerone, S., Barlera, S., and Maggioni, A. P., Clinical and epidemiological characteristics of juvenile myocardial infarction in Italy: The GISSI experience. G. Ital. Cardiol. 28(5):505–512, 1998.
18.
Doughty, M., Mehta, R., Bruckman, D., Das, S., Karavite, D., Tsai, T., and Eagle, K., Acute myocardial infarction in the young-The University of Michingan experience. Am. Heart J. 143(1):56–62, 2002.CrossRef
19.
Agrawal, R., and Srikant, R., Fast algorithms for mining association rules in large databases. Int. Conf. Very Large Data Bases. 487–99, 1994.
20.
Han, J., Pei, J., and Yin, Y., Mining frequent patterns without candidate generation. ACM SIGMOD Int. Conf. Manag. Data 29(2):1–12, 2000.
21.
22.
Rauch, J., and Šimůnek, M., Alternative approach to mining association rules. Found. Data Min. Knowl. Disc. 6:211–31, 2005.
23.
Dogan, S., and Turkoglu, I., Diagnosing hyperlipidemia using association rules. Math. Comput. Appl. 13(3):193–202, 2008.
24.
Ordonez, C., Association rule discovery with the train and test approach for heart disease prediction. IEEE Trans. Inf. Technol. Biomed. 10(2):334–343, 2006.MathSciNetCrossRef
25.
Gamberger, D., Lavrač, N. and Jovanoski, V., High confidence association rules for medical diagnosis. Intell. Data Anal. Med. Pharmacol. 42–51, 1999.
26.
Szathmary, L., Napoli, A., and Valtchev, P., Towards rare itemset mining. Int. Conf. Tools with Artificial Interlligence. 1:305–312, 2007.CrossRef
27.
Szathmary, L., Valtchev, P., and Napoli, A., Finding minimal rare itemsets and rare association rules. Knowl. Sci. Eng. Manag. 6291:16–27, 2010.
28.
Patil, S. B., and Kumaraswamy, Y. S., Extraction of significant patterns from heart disease warehouses for heart attack prediction. Int. J. Comput. Sci. Netw. Secur. 9(2):228–235, 2009.
29.
Burdick, D., Calimlim, M., Flannick, J., Gehrke, J., and Yiu, T., MAFIA: A Maximal Frequent Itemset Algorithm. IEEE Trans. Knowl. Data Eng. 17(11):1490–1504, 2005.CrossRef
30.
Karaolis, M., Moutiris, J. A., Papaconstantinou, L. and Pattichis, C. S., Association rule analysis for the assessment of the risk of coronary heart events. IEEE Eng. Med. Biol. Soc. 6238–41, 2009.
31.
Pasquier, N., Taouil, R., Bastide, Y., Stumme, G., and Lakhal, L., Generating a condensed representation for association rules. J. Intell. Inform. Syst. 24(1):29–60, 2005.MATHCrossRef
32.
Brisson, L., Pasquier, N., Hebert, C., and Collard, M., HASARD: Mining sequential association rules for atherosclerosis risk factor analysis. Eur. Conf. Princ. Pract. Knowl. Discov. Databases. 14–25, 2004.
33.
Lavrač, N., Železný, F., and Flach, P. A., RSD: Relational subgroup discovery through first-order feature construction, Lecture Notes in Computer Science, vol. 2583. Springer, Berlin Heidelberg New York, pp. 149–165, 2003.
34.
Kléma, J., Holas, T., Železný, F., and Karel, F., Mining the strongest patterns in medical sequential data. Eur. Med. Biol. Eng. Conf. 2005.
35.
Karaolis, M., Moutiris, J. A., Papaconstantinou, L. and Pattichis, C. S., AKAMAS: Mining association rules using a new algorithm for the assessment of the risk of coronary heart events. Inf. Technol. Appl. Biomed. 1–6, 2009.
36.
Delgado, M., Sánchez, D., Martín-Bautista, M. J., and Vila, M., Mining association rules with improved semantics in medical databases. Artif. Intell. Med. 21:241–245, 2001.CrossRef
37.
Kim, H. K., Jeong, M. H., Ahn, Y., Kim, J. H., Chae, S. C., Kim, Y. J., Hur, S. H., Seong, I. W., Hong, T. J., Choi, D. H., Cho, M. C., Kim, C. J., Seung, K. B., Chung, W. S., Jang, Y. S., Rha, S. W., Bae, J. H., Cho, J. G., and Park, S. J., Other Korea Acute Myocardial Infarction Registry Investigators: Hospital discharge risk score system for the assessment of clinical outcomes in patients with acute myocardial infarction (Korea Acute Myocardial Infarction Registry [KAMIR] score). Am. J. Cardiol. 107(7):965–971, 2011.CrossRef
38.
Sim, D. S., Jeong, M. H., and Kang, J. C., Current management of acute myocardial infarction: Experience from the Korea Acute Myocardial Infarction Registry. J. Cardiol. 56(1):1–7, 2010.CrossRef
39.
Ridker, P. M., Hennekens, C. H., Buring, J. E., and Rifai, N., C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N. Engl. J. Med. 342(12):836–843, 2000.CrossRef
40.
Ridker, P. M., Cushman, M., Stampfer, M. J., Tracy, R. P., and Hennekens, C. H., Inflammation, aspirin, and the risk of cardiovascular disease in apparently healthy men. N. Engl. J. Med. 336(14):973–979, 1997.CrossRef
41.
Anand, A. V., Muneeb, M., Divya, N., Senthil, R., Kapoor, M., Gowri, J., and Begum, T. N., Clinical significance of hypertension, diabetes and inflammation, as predictor of cardiovascular disease. Int. J. Biol. Med. Res. 2(1):369–373, 2011.
42.
Oviagele, B., Markovic, D., and Fonarow, G. C., Recent US patterns and predictors of prevalent diabetes among acute myocardial infarction patients. Cardiol. Res. Pract. 2011(145615):1–8, 2011.CrossRef
43.
Lee, M. G., Jeong, M. H., Ahn, Y., Chae, S. C., Hur, S. H., Hong, T. J., Kim, Y. J., Seong, I. W., Chae, J. K., Rhew, J. Y., Chae, I. H., Cho, M. C., Bae, J. H., Rha, S. W., Kim, C. J., Choi, D., Jang, Y. S., Yoon, J., Chung, W. S., Cho, J. G., Seung, K. B., and Park, S. J., Comparison of clinical outcomes following acute myocardial infarctions in hypertensive patients with or without Diabetes. Korean Circ. J. 39(6):243–250, 2009.CrossRef
44.
Kang, D. G., Jeong, M. H., Ahn, Y., Chae, S. C., Hur, S. H., Hong, T. J., Kim, Y. J., Seong, I. W., Chae, J. K., Rhew, J. Y., Chae, I. H., Cho, M. C., Bae, J. H., Rha, S. W., Kim, C. J., Jang, Y. S., Yoon, J., Seung, K. B., and Park, S. J., Clinical effect of hypertension on the mortality of patients with acute myocardial infarction. J. Korean Sci. 24(5):800–806, 2009.CrossRef
45.
Quinlan, J. R., C4.5: Programs for Machine Learning. Morgan Kaufmann. 1993.
46.
Piatetsky-Shapiro, G., Discovery, analysis, and presentation of strong rules. Knowl. Disc. Databases 229:229–248, 1991.
47.
Brin, S., Motwani, R., Ullman, J. D., and Tsur, S., Dynamic itemset counting and implication rules for market basket data. ACM SIGMOD Int. Conf. Manag. Data 26(2):255–264, 1997.CrossRef
48.
Tungsubutra, W., Tresukosol, D., Buddhari, W., Boonsom, W., Sanguanwang, S., and Srichaiveth, B., Acute Coronary Syndrome in Young Adults: The Thai ACS Registry. J. Med. Assoc. Thai. 1:81–90, 2007.
49.
Kanitz, M. G., Giovannucci, S. J., Jones, J. S., and Mott, M., Myocardial Infarction in Young Adults: Risk Factors and Clinical Features. J. Emerg. Med. 14(2):139–145, 1996.CrossRef
50.
Hong, M. K., Cho, S. Y., Hong, B. K., Chang, K. J., Chung, M. I., Lee, H. M., Lim, W. S., Kwon, H. M., Jang, Y. S., and Chung, N. S., Acute myocardial infarction in the young adults. Yonsei Med. J. 35(2):184–189, 1994.
51.
Caimi, G., Valenti, A., and Lo Presti, R., Acute myocardial infarction in young adults: Evaluation of the haemorheological pattern at the initial stage, after 3 and 12 months. Ann. Ist Super Sanita. 43(2):139–143, 2007.
52.
Lin, Y., Hsu, L., Ko, Y., Kuo, C., Chen, W., Lin, C., Pan, W., and Chang, C., Impact of conventional cardiovascular risk factors on acute myocardial infarction in young adult Taiwanese. Acta Cardiol Sin. 26:228–234, 2010.
Metadata
Title
Discovering Medical Knowledge using Association Rule Mining in Young Adults with Acute Myocardial Infarction
Authors
Dong Gyu Lee
Kwang Sun Ryu
Mohamed Bashir
Jang-Whan Bae
Keun Ho Ryu
Publication date
01-04-2013
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 2/2013
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-012-9896-1

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