Blood Pressure Classification Using the Method of the Modular Neural Networks

Abstract

In this paper, we present a new model based on modular neural networks (MNN) to classify a patient's blood pressure level (systolic and diastolic pressure and pulse). Tests are performed with the Levenberg-Marquardt (trainlm) and scaled conjugate gradient backpropagation (traincsg) training methods. The modular neural network architecture is formed by three modules. In the first module we consider the diastolic pressure data; in the second module we use details of the systolic pressure; in the third module, pulse data is used and the response integration is performed with the average method. The goal is to design the best MNN architecture for achieving an accurate classification. The results of the model show that MNN presents an excellent classification for blood pressure. The contribution of this work is related to helping the cardiologist in providing a good diagnosis and patient treatment and allows the analysis of the behavior of blood pressure in relation to the corresponding diagnosis, in order to prevent heart disease.

Figure 1

General scheme of the method.

Figure 2

Real data of the patients.

Figure 3

The best architecture for MNN with the training method (trainlm).

Figure 4

The best architecture for MNN with the training method (traincsg).

Figure 5

Modeling of diastolic pressure with the MNN.

Figure 6

Modeling of systolic pressure with the MNN.

Figure 7

MNN modeling of pulse pressure.