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  1. Association Rule Mining tools predict the association of early-onset Myocardial Infarction with Hypertension and Diabetes Mellitus.

  2. Association Rule Mining tools using clinical and biochemical attributes can predict the development of Hypertension and Diabetes Mellitus in Myocardial Infarction patients.


Cardiovascular diseases (CVDs) are a major cause of mortality in diabetic patients. Hypertensive patients are more likely to develop diabetes and hypertension contributes to the high prevalence of CVDs, in addition to dyslipidemia and smoking. This study was to find the different patterns and overall rules among CVD patients, including rules broken down by age, sex, cholesterol and triglyceride levels, smoking habits, myocardial infarction (MI) type on ECG, diabetes, and hypertension. The cross-sectional study was performed on 240 subjects (135 patients of ST-elevation MI below 45 years and 105 age matched controls). Association rule mining was used to detect new patterns for early-onset myocardial infarction. A hotspot algorithm was used to extract frequent patterns and various promising rules within real medical data. The experiment was carried out using "Weka'', a tool for extracting rules to find out the association between different stored real parameters. In this study, we found out various rules of hypertension like “Rule 6” says that if levels of BP Systolic > 131 mmHg, LpA2 > 43.2 ng/ml, hsCRP > 3.71 mg/L, initial creatinine > 0.5 mg/dl, and initial Hb ≤15 g/dl (antecedent), then the patient will have 88% chance of developing hypertension (consequent). Similarly for diabetes mellitus with finding their lift and confidence for different support like “Rule 6”, if MI type on ECG = ’Inferior Wall MI’ with STATIN=No, and levels of Triglycerides ≤325 (antecedent), then the patient had a 67% chance of developing diabetes mellitus. We concluded that early-onset myocardial infarction is significantly associated with hypertension and diabetes mellitus.Using association rule mining, we can predict the development of hypertension and diabetes mellitus in MI patients.


Cardiovascular diseases diabetes hypertension association rule mining hotspot myocardial infarction

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How to Cite
Singh, A., Singh, D., Sharma, S. ., Upreti, K., Maheshwari, M., Mehta, V., Sharma, J., Mehra, P. ., & Dabla, P. K. (2022). Discovering Patterns of Cardiovascular Disease and Diabetes in Myocardial Infarction Patients Using Association Rule Mining. Folia Medica Indonesiana, 58(3), 242–250.


  1. Agapito G, Calabrese B, Guzzi P, et al (2019). Association rule mining from large datasets of clinical invoices document. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE.
  2. An J, Chen Y, Chen H (2005). DDR: An index method for large time-series datasets. Inf. Syst. 30, 333–348.
  3. Chan C, Chen W, Kuo H (2012). Circadian variation of acute myocardial infarction in young people. Am. J. Emerg. Med. 30, 1461–1465.
  4. Chan M, Woo K, Wong H, et al (2006). Antecedent risk factors and their control in young patients with a first myocardial infarction. Singapore Med. J. 47, 27–30.
  5. Chen Y, Chen F (2008). Identifying targets for drug discovery using bioinformatics. Expert Opin. Ther. Targets 12, 383–389.
  6. Dabla P, Sharma S, Saurabh K, et al (2021). Atherogenic index of plasma: A novel biomarker and lipid indices in young myocardial infarction patients. Biomed. Biotechnol. Res. J. 5, 184–190.
  7. Dai W, Long J, Cheng Y, et al (2018). Elevated plasma lipoprotein(a) levels were associated with increased risk of cardiovascular events in Chinese patients with stable coronary artery disease. Sci. Rep. 8, 1–9.
  8. Doughty M, Mehta R, Bruckman D, et al (2002). Acute myocardial infarction in the young - The University of Michigan experience. Am. Heart J. 143, 56–62.
  9. George M, Tong X, Kuklina E, et al (2011). Trends in stroke Hospitalizations and associated risk factors among children and young adults, 1995-2008. Ann. Neurol. 70, 713–721.
  10. Gupta A, Wang Y, Spertus J, et al (2014). Trends in acute myocardial infarction in young patients and differences by sex and race, 2001 to 2010. J. Am. Coll. Cardiol. 64, 337–345.
  11. Han J, Kamber M, Pei J (2011). Data mining: Concepts and techniques, third edition. Morgan Kaufmann, Massachusetts.
  12. Hastie T, Tibshirani R, Friedman J (2009). The elements of statistical learning: Data mining, inference, and prediction, second edition. Springer, New York.
  13. Hipp J, Güntze U, Nakhaeizadeh G (2000). Algorithms for association rule mining - A general survey and comparison. ACM SIGKDD Explor. Newsl. 2, 58–64.
  14. Joshi P, Islam S, Pais P, et al (2007). Risk factors for early myocardial infarction in South Asians compared with individuals in other countries. JAMA 297, 286–294.
  15. Lau R, Tang M, Wong O, et al (2006). An evolutionary learning approach for adaptive negotiation agents. Int. J. Intell. Syst. 21, 41–72.
  16. McManus D, Piacentine S, Lessard D, et al (2011). Thirty-year (1975 to 2005) trends in the incidence rates, clinical features, treatment practices, and short-term outcomes of patients<55 years of age hospitalized with an initial acute myocardial infarction. Am. J. Cardiol. 108, 477–482.
  17. McNamara K, Alzubaidi H, Jackson J (2019). Cardiovascular disease is a leading cause of death: How are pharmacists getting involved? Integr. Pharm. Res. Pract. 8, 1–11.
  18. Mohan H (2005). Textbook of pathology. Anshan Publishers, Tunbridge Wells.
  19. Oliveira A, Barros H, Azevedo A, et al (2009). Impact of risk factors for non-fatal acute myocardial infarction. Eur. J. Epidemiol. 24, 425–432.
  20. Ordonez C (2006). Association rule discovery with the train and test approach for heart disease prediction. In: Transactions on Information Technology in Biomedicine. IEEE, pp. 334–343.
  21. Patil B, Joshi R, Toshniwal D (2010). Association rule for classification of type-2 diabetic patients. In: Second International Conference on Machine Learning and Computing. pp. 330–334.
  22. Shehabi S, Baba A (2021). MARC: Mining association rules from datasets by using clustering models. Int. J. Multidiscip. Stud. Innov. Technol. 5, 89–93.
  23. Simon G, Schrom J, Castro M, et al (2013). Survival association rule mining towards type 2 diabetes risk assessment. AMIA Annu. Symp. Proc. Arch. 16, 1293–1302.
  24. Soni U, Behara S, Krishnan K, et al (2016). Application of association rule mining in risk analysis for diabetes mellitus. Int. J. Adv. Res. Comput. Commun. Eng. 5, 548–551.
  25. Srinath R, Shah B, Varghese C, et al (2005). Responding to the threat of chronic diseases in India. Lancet 366, 1744–1749.
  26. Szathmary L, Valtchey P, Napoli A (2010). Generating rare association rules using the minimal rare itemsets family. Int. J. Softw. Informatics 4, 219–238.
  27. Tandan M, Acharya Y, Pokharel S, et al (2021). Discovering symptom patterns of COVID-19 patients using association rule mining. Comput. Biol. Med. 131, 1–12.
  28. Vasoya A, Koli N (2016). Mining of association rules on large database using distributed and parallel computing. In: 7th International Conference on Communication, Computing and Virtualization. pp. 221–230.
  29. Virani S, Alonso A, Aparicio H, et al (2021). Heart disease and stroke statistics - 2021 update: A report from the American Heart Association. Circulation 143, 254–743.