{'fraction_non_alphanumeric': 0.021245059288537548, 'fraction_numerical': 0.014822134387351778, 'mean_word_length': 4.9734513274336285, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
OS 26-01 CLASSIFICATION OF ARTERIAL HYPERTENSION USING A COMPUTATIONAL MODEL BASED ON ARTIFICIAL MODULAR NEURAL NETWORKS

Objective: The development of an artificial modular neural network (MNN) method for diagnosing and classification of arterial Hypertension based on the level of the blood pressure (BP) of a patient is presented. The main goal is to diagnose the degree of hypertension based on the BP values using MNN applying response integration via a gating network approach. Design and Method: This study was performed with 28 patients to classify the BP 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. The proposed computational method using Artificial MNN consists on designing the best system architecture of the MNN based on previous data. Results: Based on ABPM data of the 28 patients, we implemented a computational system based on three different MNN architectures. The first one achieved a classification rate of 93.3%, the second one of 91.7% and the third one of 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 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. We can note that MNNs have proven to be a reliable and accurate technique when compared to conventional classification methods.