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Using new information technology to predict the mechanism and characteristics of chronic liver disease is an effective way to improve its diagnosis. In this paper, we used the principal component analysis (P C A ) of the machine learning algorithm to reduce the dimensional indicators of chronic liver disease, combined with neural network learning to build a new intelligent prediction of chronic liver disease (IPCLD). The experiment studied 125 data sets of 20-di­ mensional indicators of chronic liver disease, used receiver operating characteristic (R O C ) curve to select 13-dimensional more sensitive indicators ? further reduces the dimension down to 5 by P C A . The neural network is trained with 115 data sets,and the remaining 10 data sets are used as test data sets. Compared with being trained by original data, the IPCLD improves 15. 0 7 % prediction accuracy and reduces the complexity.

基于主成分机器学习算法的慢性肝病的智能预测新方法 (New Intelligent Prediction of Chronic Liver Disease Based on Principal Component Machine Learning Algorithm).