Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30528
An ANN-Based Predictive Model for Diagnosis and Forecasting of Hypertension

Authors: V. Balanica, O. O. Obe, E. Neagoe

Abstract:

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.

Keywords: Neural Network, Hypertension, Supervised Learning, data set, training set

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1338220

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1322

References:


[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.