Since the previous research works are not synthetic and accurate enough for building a precise hypertension risk evaluation system,by ranking the significances for hypertension factors according to the information gains on 2 231 normotensive and 823 hypertensive samples,totally 42 different neural network models are built and tested.The prediction accuracy of a model whose inputs are 26 factors is found to be much higher than the 81.61% obtained by previous research work. The prediction matching rates of the model for ＂hypertension or not＂,＂systolic blood pressure＂,and ＂diastolic blood pressure＂ are 95.79%,98.22% and 98.41%,respectively.Based on the found model and the object oriented techniques,an online hypertension risk evaluation system is developed,being able to gather new samples,learn the new samples,and improve its prediction accuracy automatically.
