Affiliations: [a] CVR College of Engineering, Hyderabad, India | [b] Department of CSE, CBIT, Hyderabad, India
Corresponding author: N. Satyanarayana, CVR College of Engineering, Hyderabad, India. E-mail: [email protected].
Abstract: High Blood Pressure (HBP) is one of the major triggering factors for many health-related issues such as brain stroke, heart stroke, kidney failure, eye damage, etc. The victims of HBP are drastically increasing day by day across the globe. The prediction of HBP in advance is more beneficial to control the Blood Pressure (BP) rather than using BP control medications. So this paper focused on an intelligent fuzzy classification model called Association based Fuzzy rule Miner (AFM) to predict HBP. Although they are numerous parameters that contribute to HBP, the impact of Bio-Psychological factors on HBP is always worth noting. This paper considered biological factors obesity level, cholesterol level, age, and Psychological factors anxiety level and anger level of a person for experimental analysis. The proposed Model initially converts the crisp data set into the fuzzified data set. Later, the association rules are extracted using apriori algorithm based on conditions imposed as constraints. In the final step the extracted association rules for each decision class separately together constructs AFM, which predicts whether a person is a victim of HBP or not. The experiments are conducted on a real-time dataset of 1000 records, where 600 records are used for training and 400 records are used for testing. The AFM has shown 90.75% accuracy, which is for better than the accuracy of existing classifiers such as Random Forest, Naïve Bayes, Simple logistic regression, J48, and PART.
Keywords: Association, blood pressure, fuzzy based systems, classification, apriori