Cardiovascular diseases and hypertension are among the prevalent health issues in various societies, which can lead to serious consequences such as heart attacks and strokes. Various factors, such as high cholesterol, hypertension, and smoking, can influence the occurrence of these diseases. The objective of this study is to identify and analyze the relationships among different health features, including high cholesterol, hypertension, smoking, and heart disease, using association rule mining. The present research employed the Association Rule Mining algorithm to identify relationships among various features in health data. The study dataset, titled CDC Diabetes Health Indicators, included ۲۵۳,۶۸۰ samples and ۲۱ features, which have also been used in previous studies. The data features included hypertension (HighBP), high cholesterol (HighChol), smoking (Smoker), and a history of heart disease (HeartDisease or Attack). Association rules were extracted using metrics such as Support, Confidence, Lift, Leverage, and Conviction. The association rule analysis revealed that a history of heart disease (HeartDisease or Attack), either alone or combined with cholesterol screening (CholCheck), has a strong positive association with hypertension (HighBP) (Confidence > ۰.۸۱, Lift = ۱.۴۵). Additionally, high cholesterol (HighChol), whether alone or in combination with behavioral factors such as smoking and health monitoring, was associated with a higher probability of hypertension (Confidence between ۰.۷۱ and ۰.۷۴, and Lift between ۱.۲۶ and ۱.۳۰). The results demonstrated that association rule mining can be a valuable tool for identifying hidden relationships among various health features. Notably, significant associations were found among high cholesterol, hypertension, smoking, and heart disease, which can serve as a basis for preventing cardiovascular diseases. The findings of this study can assist physicians and health professionals in making better decisions for disease prevention and management, as well as in identifying high-risk groups.
