Artificial neural networks as an engine of Internet based hypertension prediction tool

Hypertension is the most common cause of death. Therefore it is recognized as a major civilization disease next to diabetes, hyperuricemia, asthma etc. The objective was to use artificial neural networks (ANNs) to handle demographic data and to produce system of hypertension risk prediction. Database used in the development of hypertension risk model was obtained from CDC (BRFSS--Behavioral Risk Factor Surveillance System). Screening for optimal ANN architecture was performed among various backpropagation and fuzzy neural networks with use of 10-fold cross-validation scheme. Single ANNs as well as experts committees were tested. Best results were found to be around 75%--expressed as total classification rate. Java applet was designed to be the interface between ANN system and end user. Spreadsheet form was chosen to facilitate navigation and used by healthcare non-specialists. Free of charge Internet publication is expected soon at the address [url: see text].