Zhang Xun-bao, Huang Shui-ping, Zhuo Lang, Wu Xiu-juan, Sun Gui-xiang, Zhao Hua-shuo, Li Lei
Department of Medical Statistics and Epidemiology, School of Public Health, Xuzhou Medical College, Xuzhou 221002, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2008 Oct;29(10):1038-41.
To introduce a method of classification with high precision--the artificial neural network (ANN), and to compare the results using logistic regression method. Using data from 1070 landless peasants' mental health survey, the artificial neural network models and logistic regression model were built and compared on their advantages and disadvantages of the two models. The prediction accuracy for artificial neural network was 94.229% and for logistic regression it was 51.028%. ANN appeared to have had good ability on generalization. ANN displayed advantages when conditions of classical statistical techniques could not be met or the predictive effect appeared to be unsatisfactory. Hence, ANN would make a better fracture of its application in medical research.
介绍一种高精度分类方法——人工神经网络(ANN),并比较使用逻辑回归方法的结果。利用1070名失地农民心理健康调查的数据,构建人工神经网络模型和逻辑回归模型,并比较两种模型的优缺点。人工神经网络的预测准确率为94.229%,逻辑回归的预测准确率为51.028%。人工神经网络似乎具有良好的泛化能力。当经典统计技术的条件不满足或预测效果不理想时,人工神经网络显示出优势。因此,人工神经网络在医学研究中的应用前景较好。