High Blood Pressure (HBP) is a state in the biological system of human beings developed due to physical and psychological changes. Nowadays, it is a most prevalent problem in human beings irrespective of age, place, and profession. The HBP victims are increasing rapidly across the globe. HBP is undiagnosed in the majority of the patients because most of the affected people are not aware of it. To overcome this problem, this paper proposes a new approach that uses ABF (Arterial Blood Flow)-function to predict a person is prone to HBP. In this approach, the impact factor for each attribute is calculated based on the attribute value. Both attribute value and corresponding impact factor are used by ABF function to predict a person is prone to HBP. We experimented proposed approach on real-time data set, which consists of 1100 patient records in the age group between 18 and 65. Our approach outperforms regarding predictive accuracy over j48, Naive Bayes and Rule-based classifiers.