Journal of Capital Medical University ›› 2022, Vol. 43 ›› Issue (3): 407-414.doi: 10.3969/j.issn.1006-7795.2022.03.013

• Integrative Diagnosis and Treatment for Mental Disorder • Previous Articles     Next Articles

Classification model of pulse parameters of unipolar depression and bipolar depression based on machine learning

Liu Xinzi1,2, Li Ziyan1,2, Zheng Sisi1,2, Zhu Hong1,2, Yin Dongqing1,2, Jia Hongxiao1,2*   

  1. 1. The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, China;
    2. Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100069, China
  • Received:2022-02-20 Online:2022-06-21 Published:2022-06-01
  • Contact: *E-mail:jhxlj@ccmu.edu.cn
  • Supported by:
    Capital's Funds for Health Improvement and Research (2020-4-2123),Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support (ZYLX202129),Beijing Hospitals Authority's Ascent Plan (DFL20191901).

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Abstract: Objective To study the characteristic difference of pulse parameters between unipolar depression and bipolar depression based on machine learning method. Methods Thirty-one patients with unipolar depression and 57 patients with bipolar depression were collected with Daosheng DS01-A TCM four diagnostic instruments, and the pulse parameters were compared with SPSS 26. The models of support vector machines (SVM) and random forest (RF) were established with R 4.0.5. We evaluated the model performance and obtained the importance ranking of model features. Results There were significant differences in pulse force, pulse rate, AD, T1/ T4, H1, H3/ H1, and (H3-H1)/ H1 between unipolar depression and bipolar depression (P<0.05). Both SVM model and RF model have good accuracy and stability. The accuracy of SVM model is 80.56%, area under curve (AUC) value is 83.04%. The accuracy of RF model is 80.56%, AUC value is 84.62%. The top 5 importance of model features are H4, H2, AD, AGE, (T4-T1)/T and H3, H4, AD, AGE, H2 respectively. Conclusion Unipolar depression and bipolar depression have obvious differences in pulse parameters, which can assist clinicians in the differentiation of unipolar and bipolar depression.

Key words: unipolar depression, bipolar depression, pulse parameters, machine learning

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