The development of an artificial intelligence method to diagnose and classify the arterial Hypertension based on the level of the blood pressure (BP) of a patient. The main goal is to diagnose the degree of Hypertension based on the BP values using a modular neural network (MNN) applying response integration via the average method.Design and method:
This study was performed with 28 patients to classify the blood pressure levels, based on the European Society of Hypertension (ESH) and the European Society of Cardiology (ESC) Guidelines of Hypertension. We collected patient data with the Ambulatory blood pressure monitoring (ABPM), which is a tool that can help diagnose hypertension. The main goal is to model the 24-hr ABPM patterns in patients with the MNN and classify the BP of the patient using the Levenberg-Marquardt algorithm, which is a good method that guarantees a high learning speed in the MNN. The proposed computational method using Artificial Intelligence techniques consists on designing the best system architecture of the MNN based on previous data for classification the levels of BP of each patients.Results:
Based on the data of the ABPM in the 28 patients, we built a computational system using three different MNN architectures. The first one achieved a classification rate of 93.3%, the second one a 91.7% and the third one a 89.7% respectively, and with this we can note that excellent classifications results are obtained. The best architecture of the MNN for achieving these results is the following: 25 neurons in the first layer and 30 neurons for the second layer for each of the modules, and the Target Error for learning is of 0.002 and 500 epochs are used during learning.Conclusions:
We can conclude that with the proposed method using a MNN and the European Guidelines of Hypertension, good results are obtained for diagnosis and classification of hypertension. In this case we can note that MNNs have proven to be a reliable and accurate technique when compared to conventional classification methods for this problem and decrease the inter observer variability.