基于径向基概率神经网络模型的小儿厌食症状辅助诊断

结合了径向基神经网络较强模式分类能力与概率神经网络运算简单的优点,提出了一种径向基概率神经网络模型,并应用于小儿厌食症的辅助诊断,通过对119例样本数据的处理,获得了92.4%的准确率.此外,偏最小二乘法的分析结果表明,Zn元素与小儿厌食症关系最为紧密.

Assisted diagnosis for infancy anorexia based on a radial basis function probabilistic neural network model

Based on a radial basis function probabilistic neural network model, which combined the powerful capability of the pattern classification of radial basis function neural network and the simple operation of probabilistic neural network, a new approach of assisted diagnosis for infancy anorexia was developed and applied to 119 samples, with an accuracy rate of 92%. In addition, the result of partial least squares analysis indicated that Zn was the most important element that was closely related to infancy anorexia..