论文部分内容阅读
本文采用基于佳点集理论的改进型BP神经网络,建立一个慢性肾脏病的预测模型。以此来弥补人们的慢性肾脏病诊断方面的不足,为医务人员对慢性肾脏病的判断提高有力依据。1.引言近年来肾脏病的患病率逐年升高,慢性肾脏病的病因除了与生活水平有关,还与地域,性别,人种等因素有关[1]。据有关资料显示中国的慢性肾脏病患者高达1.2亿占全国总人口的9.2%,而且我国的医疗水平与发达国家相比还有很大的差距[2],所以说
In this paper, an improved BP neural network based on good point set theory is used to establish a prediction model of chronic kidney disease. In order to make up for people’s lack of diagnosis of chronic kidney disease, for medical staff to improve the judgment of chronic kidney disease. 1. Introduction In recent years, the prevalence of kidney disease increased year by year, in addition to the cause of chronic kidney disease and living standards, but also with the geographical, gender, ethnic and other factors [1]. According to relevant data, up to 120 million people with chronic kidney disease in China account for 9.2% of the total population in the country, and there is still a big gap between the medical treatment level in China and the developed countries [2]