Discovering Patterns of Cardiovascular Disease and Diabetes in Myocardial Infarction Patients Using Association Rule Mining

Cardiovascular diseases diabetes hypertension association rule mining hotspot myocardial infarction

Authors

  • Anju Singh Sage University, Bhopal, India
  • Divakar Singh Barkatullah University Institute of Technology, Barkatullah University, Bhopal, India
  • Shikha Sharma Department of Biochemistry, Chacha Nehru Bal Chikitsalaya, New Delhi, India
  • Kamal Upreti Dr. Akhilesh Das Gupta Institute of Technology & Management, New Delhi, India
  • Manish Maheshwari Makhanlal Chaturvedi National University of Journalism and Communication, Bhopal, India
  • Vimal Mehta Department of Cardiology, Govind Ballabh Pant Institute of Postgraduate Medical Education & Research/ Associated Maulana Azad Medical College, New Delhi, India
  • Jitender Sharma Department of Biochemistry, Govind Ballabh Pant Institute of Postgraduate Medical Education & Research/ Associated Maulana Azad Medical College, New Delhi, India
  • Pratishtha Mehra Department of Cardiology, Govind Ballabh Pant Institute of Postgraduate Medical Education & Research/ Associated Maulana Azad Medical College, New Delhi, India
  • Pradeep Kumar Dabla
    pradeep_dabla@yahoo.com
    Department of Biochemistry, Govind Ballabh Pant Institute of Postgraduate Medical Education & Research/ Associated Maulana Azad Medical College, New Delhi, India https://orcid.org/0000-0003-1409-6771
Vol. 58 No. 3 (2022): September
Original Research Report
September 5, 2022

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Highlights:

  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.


Abstract:

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.