Predicting High Blood Pressure Using Decision Tree-Based Algorithm
High blood pressure, also called as hypertension, is a state developed in biological system of human beings by knowingly or unknowingly. It may occur due to varied biological and psychological reasons. If high blood pressure state is sustained for a longer cycle, then the person may be the victim of heart attack or brain stroke or kidney disease. This paper uses a decision tree-based J48 algorithm, to predict whether a person is prone to high blood pressure (HBP). In our experimental analysis, we have taken certain biological parameters such as age, obesity level, and total blood cholesterol level. We have taken the real-time data set of 1045 diagnostic records of patients in the age between 18 and 65. These are collected from a medical diagnosis center Doctor C, Hyderabad. Records (66%) are used to train the model, and remaining 34% records are used to test the model. Our results showed 88.45% accuracy.
KeywordsClassification Decision tree Blood pressure monitoring
- 6.Liu, J.: Model-based oscillometric blood pressure measurement: preliminary validation in humans. In: Proceedings of IEEE Conference on Engineering, Medical Biological Soc, pp. 1961–1964 (2014)Google Scholar
- 8.Satyanarayana, N., Ramalingaswamy, C.H., Ramadevi, Y.: Survey of classification techniques in data mining. Int. J. Innov. Sci. Eng. Technol. 1, 268–278 (2014)Google Scholar