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J. Ma, A. R. Folson, S. L. Melnick, J. H. Eckfeldt, A. R. Sharrett, A. A. Nabulsi, et al., (1995) Associations of serum and dietary magnesium with cardiovascular disease, hyper- tension, diabetes, insulin, and carotid arterial wall thickness: The ARIC Study. J. Clin. Epidemiol., 48, 927-940.

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• TITLE: Modelling of the relationship between systolic blood pressure and glucose with the magnesium ion present in the blood plasma: an approach using artificial neural networks

  AUTHORS: Júlio C. D. Conway, Stefania N. Lavorato, Vinícius F. Cunha, Jadson C. Belchior

  KEYWORDS: Artificial Neural Networks; Blood Plasma; Magnesium; Systolic Blood Pressure

  JOURNAL NAME: Health, Vol.1 No.3, December 1, 2009

  ABSTRACT: Artificial neural networks became an attractive alternative for modeling and simulation of com- plex biological systems. In the present work, a blood plasma model based on artificial neural networks was proposed in order to evaluate the relationship between the magnesium ion pre-sent in the blood plasma and systolic blood pressure and glucose. Experimental and simu- lated data were used to construct and validate the model. It performed the analysis consider-ing the systolic blood pressure and glucose as a function of magnesium ion concentration at a fixed temperature (37oC). Predictions of these relationships through the proposed model produced errors, on average, below 1% com-pared against experimental data not presented in the training step. The proposed methodology revealed quantitative results and correctly pre-dicted behaviors and trends towards the asso-ciation between magnesium concentrations and systolic blood pressure, and glucose in far agreement with experimental results from lit-erature. These results indicated that artificial neural networks can successfully learn the complexity of the relationships among bio-logical parameters of distinct groups and can be used as a complementary tool to assist studies in which the role of magnesium in systolic blood pressure and glucose are con-sidered.