慢性稳定性心绞痛是严重危害人类健康的心血管疾病，其诊断的“金标准”冠状动脉造影具有损伤性且经济成本较高。穴位敏化是穴位随着脏腑功能变化而呈现出的不同功能状态，基于穴位敏化现象来探知脏腑的不同功能状态，可能为病情判断与疾病诊断提出新思路。人工神经网络技术是当前信息学科的研究热点，该技术提供了1种基于样本驱动实现模式分类的通用方法。本文提出1种基于人工神经网络技术与穴位敏化理论的慢性稳定性心绞痛疾病预测模型构建方法，该方法为慢性稳定性心绞痛疾病的辅助预测诊断提供新策略，亦为传统中医理论（穴位敏化理论）与现代信息技术（人工神经网络）的交叉融合提供新思路。

Chronic stable angina pectoris is a cardiovascular disease that seriously endangers human health, and the "gold standard" of diagnosis, coronary angiography, is invasive and economically expensive. Acupuncture point sensitization is the different functional states of acupuncture points in response to changes in the function of the zang-fu viscera and exploring the different functional states of the internal organs based on acupuncture point sensitization may provide new ideas for disease diagnosis. Artificial neural network (ANN) technology is a current research hotspot in the information discipline and this technology provides a general method to achieve pattern classification based on sample. In this paper, we propose a method to construct a prediction model for chronic stable angina pectoris based on artificial neural network technology and acupoint sensitization theory, which provides a new strategy to assist in the prediction and diagnosis of chronic stable angina pectoris and a new idea to cross-fertilize traditional Chinese medicine theory (acupoint sensitization theory) with modern information technology (ANN).

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