Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning
Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning.
Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest.
Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment.
Conclusions: The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.
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Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
On 06 Jan, 2021
On 07 Dec, 2020
Invitations sent on 10 Nov, 2020
On 09 Nov, 2020
On 09 Nov, 2020
On 09 Nov, 2020
Posted 27 Oct, 2020
On 01 Nov, 2020
Received 28 Oct, 2020
Received 22 Oct, 2020
On 21 Oct, 2020
Invitations sent on 18 Oct, 2020
On 18 Oct, 2020
On 16 Oct, 2020
On 15 Oct, 2020
On 15 Oct, 2020
On 04 Sep, 2020
Received 30 Aug, 2020
Received 21 Aug, 2020
On 18 Aug, 2020
Received 06 Aug, 2020
On 01 Aug, 2020
On 23 Jul, 2020
Invitations sent on 21 Jul, 2020
On 16 Jul, 2020
On 15 Jul, 2020
On 15 Jul, 2020
On 14 Jul, 2020
Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning
On 06 Jan, 2021
On 07 Dec, 2020
Invitations sent on 10 Nov, 2020
On 09 Nov, 2020
On 09 Nov, 2020
On 09 Nov, 2020
Posted 27 Oct, 2020
On 01 Nov, 2020
Received 28 Oct, 2020
Received 22 Oct, 2020
On 21 Oct, 2020
Invitations sent on 18 Oct, 2020
On 18 Oct, 2020
On 16 Oct, 2020
On 15 Oct, 2020
On 15 Oct, 2020
On 04 Sep, 2020
Received 30 Aug, 2020
Received 21 Aug, 2020
On 18 Aug, 2020
Received 06 Aug, 2020
On 01 Aug, 2020
On 23 Jul, 2020
Invitations sent on 21 Jul, 2020
On 16 Jul, 2020
On 15 Jul, 2020
On 15 Jul, 2020
On 14 Jul, 2020
Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning.
Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest.
Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment.
Conclusions: The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.