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A Neuro fuzzy hybrid model (NFHM) is used as a new Artificial Intelligence method to classify blood pressure (BP). The objective is to model the behavior of the blood pressure based on monitoring data of 6 days with 4 samples per day, two samples were taken at noon and two more in the afternoon. The new computational model based on the data entered to a classifier determines the level of BP to which the patient belongs.

Design and method:

Intelligent computing techniques, such as neural networks and fuzzy logic, working with a modular architecture are used to model the BP behavior. We work with different numbers of layers and some other learning parameters and we also use a fuzzy rule base with the expert knowledge of BP classification so we can achieve a more accurate modeling.


Of the 30 patients that were monitored, patients were considered in different stages, such as high BP, normal BP and low BP, this in order to use our system with patients in different conditions. Different architectures of NFHM were used to achieve better results: Architecture 1 gave better results than Architecture 2 in all 30 experiments. First we have the systolic architecture 1 with an Error Mean (EM) of 3.747% and the systolic architecture 2 with an EM of 8.471%. Finally we have the diastolic architecture 1 with an EM of 2 % and the diastolic architecture 2 with an EM of 7.241%.


This type of NFHM actually implements the human reasoning. Using a set of decision rules we can offer a diagnosis, in this case of BP classification. This is a very efficient, less time consuming and more accurate method to classify BP. Finally we can note that is a very effective method for diagnosis of hypertension or hypotension, which can help a physician or health worker to achieve better accuracy when giving a diagnosis to the patient. It can also motivate working on other kinds of tests utilizing Artificial Intelligent techniques for the diagnosis of different cardiovascular diseases.

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