An ANN-Based Predictive Model for Diagnosis and Forecasting of Hypertension

The effects of hypertension are often lethal thus its
early detection and prevention is very important for everybody. In
this paper, a neural network (NN) model was developed and trained
based on a dataset of hypertension causative parameters in order to
forecast the likelihood of occurrence of hypertension in patients. Our
research goal was to analyze the potential of the presented NN to
predict, for a period of time, the risk of hypertension or the risk of
developing this disease for patients that are or not currently
hypertensive. The results of the analysis for a given patient can
support doctors in taking pro-active measures for averting the
occurrence of hypertension such as recommendations regarding the
patient behavior in order to lower his hypertension risk. Moreover,
the paper envisages a set of three example scenarios in order to
determine the age when the patient becomes hypertensive, i.e.
determine the threshold for hypertensive age, to analyze what
happens if the threshold hypertensive age is set to a certain age and
the weight of the patient if being varied, and, to set the ideal weight
for the patient and analyze what happens with the threshold of
hypertensive age.

• O. O. Obe
• V. Balanica
• E. Neagoe

• Neural Network
• hypertension
• data set
• training set
• supervised learning.

[1] Minsky M. and Papert S. (2003), Perceptron, 2nd Edition MIT Press
Cambridge, MA Moller, Dederisksen, Soren, Lars V and Pedersan T
(2003). Tele-monitoring of Home Blood Pressure in Treatment
Hypertensive Patients, Taylor and Francis Blood Pressure Vol 12 pp 56-
62.
[2] Caironi, P.V.C, Portoni, L Combi C. Pinciroli F and Ceri S (1998).
Hypercare: A prototype of an Active Database for Compliance with
Essential Hypertension Therapy Guidelines Dipartmenti di Matermaticae
Informatica, Universita delgi studi di Udine, Udine Italy.
[3] Dumitrache, I. and Buiu, C (1995).; Hybrid Geno-fuzzy controllers;
IEEE, Intelligent Systems for the 21st Century, Vol. 5, pp. 2034-2039.
[4] Babita P. and Mishra R.B (2009); Knowledge and Intelligent Computing
System in Medicine; Computers in Biology and Medicine; Vol. 39, pp.
215-230.
[5] Alpaydin, Ethem (2010). Introduction to machine learning. s.l. : MIT
Press.