Research on Construction of Classification Model of Depression Syndrome Based on
Machine Learning
HU Guifang1, ZHANG Qijun2, CHEN Bixin2, ZOU Xiaolan1, GAO Zhiming1, ZOU Yuanjun1
1. School of Medical Information, Changchun University of Chinese Medicine, Changchun Jilin 130117, China;
2. Department of Data Management, Jilin Provincial Health Statistics Information Center, Changchun Jilin 130061, China
Abstract：Objective To construct the classification model of depression syndrome based on random forest and artificial neural
network algorithm of machine learning, and use confusion matrix to evaluate the accuracy of the model. Methods The medical
record data mainly came from ancient and modern medical record cloud platform, CNKI, Wanfang and VIP Databases, a total of 1010
medical records were included in this study. The proportion of training set and test set was 7∶3. The features was extracted by using
Python in Jupyter notebook. Then the classification model of depression syndrome was constructed by random forest and artificial
neural network, and finally the accuracy of the classification results was verified by confusion matrix. Results The overall accuracy
of the syndrome classification model constructed by random forest algorithm was 89.44%, including liver Qi stagnation (95.00%), Qi
stagnation fire (82.05%), phlegm Qi depression (89.29%), loss of mind (85.07%), heart and spleen deficiency (89.74%) and heart and
kidney Yin deficiency (95.16%). The syndrome classification model constructed by artificial neural network algorithm had an overall
accuracy of 96.03%, including liver Qi stagnation (100.00%), Qi stagnation fire (92.31%), phlegm Qi depression (96.43%), loss of
mind (91.04%), heart and spleen deficiency (97.44%) and heart and kidney Yin deficiency (100.00%). Conclusion The classification results
of the two classification models have achieved ideal results. However the accuracy of artificial neural network classification model is higher
than that of random forest classification model, and its nonlinear and fuzzy characteristics are more suitable for the classification and prediction
of traditional Chinese medicine (TCM) syndrome types. It can provide new ideas and directions for TCM diagnosis research in the future.
胡桂芳1，张启军2，陈碧心2，邹晓岚1，高芷铭1，邹元君1. 基于机器学习的郁证证型分类
模型构建研究[J]. 中国医疗设备, 2023, 38(4): 48-55.
HU Guifang1, ZHANG Qijun2, CHEN Bixin2, ZOU Xiaolan1, GAO Zhiming1, ZOU Yuanjun1. Research on Construction of Classification Model of Depression Syndrome Based on
Machine Learning. China Medical Devices, 2023, 38(4): 48-55.
