ECLAT – Based Analysis Of Heart Disease Symptom Patterns

Heart disease remains a leading global health concern, with millions of people affected worldwide. The World Health Organization has highlighted the alarming increase in heart disease, even among non-smokers. Data mining techniques, such as association rule mining, offer a promising approach to identify patterns and relationships within large datasets. In this study, the authors propose using the Eclat algorithm to efficiently discover frequent itemsets and association rules in heart disease data. By analyzing these patterns, we can gain valuable insights into the factors contributing to heart disease. The results of this analysis suggest that men are at a higher risk of heart disease compared to women, and high cholesterol levels and chest pain are common symptoms associated with the condition. This research demonstrates the potential of data mining techniques to uncover hidden patterns and improve our understanding of heart disease. By identifying key risk factors and symptoms, we can develop more effective prevention and treatment strategies.