Patterns in Risk Factors of Cardiovascular Disease using the Apriori Algorithm
Background: Cardiovascular Disease (CVD) is the dominating cause of mortality around the globe. Aim of this study was to identify the co-occurrence of risk factors of cardiovascular disease using the Apriori data mining algorithm among the patients visiting to outpatients department of a tertiary care hospital in Pakistan from January 2017 to June 2017.
Methods: This cross-sectional study includes 5,164 consecutive patients visiting to OPD of National Institute of Cardiovascular Diseases, Karachi Pakistan from January 2017 to June 2017. CVD risk factors were collected for all enrolled patients. Association rules were developed and assessed by applying data mining technique the Apriori algorithm. Pruning approaches such as removal of redundant rules, minimum length of at least two items, minimum support of 0.20, and minimum confidence of 0.90 were applied.
Results: Out of 5,164 patients 51.1% were female and 42.7% patients were more than 50 years of age. Dominantly observed risk factors are hypertension, obesity, dyslipidemia, and diabetes mellitus respectively. Hypertension was the consequent for all extracted association rules with overweight/obese, dyslipidemia, female with overweight/obese, more than 50 year, overweight/ obese with dyslipidemia, more than 50 year with dyslipidemia, female of more than 50 year of age, and female with dyslipidemia as antecedent respectively.
Conclusion: Based on the Apriori algorithm, meaningful association rules and patterns among the risk factors of cardiovascular disease (CVD) were extracted; these rules provide feasible way to reduce the risk of cardiovascular disease (CVD).