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High Blood Pressure (HBP) is a major health challenge of many around the world. Existing research covers extensively how to treat HBP, but predicting HBP in advance based on biological and psychological parameters of a person is not covered in the literature. The objective of this paper is to predict HBP based on Bio-Psychological factors of a person. Methods: We proposed an intelligent Rule-based classifier to predict HBP. The proposed model can be used to prevent HBP rather than using medication. In our approach, we considered AAA++ (Age, Anger level, Anxiety level, Obesity level (+), Cholesterol level (+)) of a person for experimental study. The proposed approach uses priority-based apriori rule pruning (PARP) classifier, which works in 3 stages. Stage 1: generate association rules using apriori. Stage 2: it uses the priority of an attribute to prune the association rules generated in stage 1. Step 3: Rules extracted in stage 2 are used to build a rule-based classifier to predict the class label of test instances. The Results of the proposed model are compared with JRip, PART, OneR and, ZeroR. Results: Experimentation is done on real-time data set using 10 fold cross-validations. In each fold, 90% data is used to train the model and 10% is used to test the model. The proposed approach has shown improved accuracy (86.4%) and reduced mean length of a rule (1.7) compared to existing rule-based algorithms. Although JRip is good at accuracy (86.9%), but the proposed model has outperformed at the mean length of the rule (1.7). Conclusion: The extracted rules after experimentation are understandable and informative to the technical and nontechnical community to predict HBP.

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