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Modelling of inquiry diagnosis for coronary heart disease in Traditional Chinese Medicine by using multi-label learning.

Liu GP, Li GZ, Wang YL, Wang YQ - BMC Complement Altern Med (2010)

Bottom Line: In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm.Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively.After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Information Access and Synthesis of TCM Four Diagnosis, Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.

ABSTRACT

Background: Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem.

Methods: Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets.

Results: A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.

Conclusions: The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.

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Results of syndrome models for inquiry diagnosis on whole labels by using ML-kNN and kNN with different k values
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Figure 2: Results of syndrome models for inquiry diagnosis on whole labels by using ML-kNN and kNN with different k values

Mentions: In order to determine whether the k value influenced the forecast results in ML-kNN, we construct models with k values as 1, 3, 5, 7, 9 and 11, respectively. Then the forecast results are listed in Figure 2 under the evaluation criteria of Average_Precision, Coverage and Ranking_Loss, respectively, where the horizontal coordinate stands for the k value, and longitudinal coordinate stands for results of Average_Precision, Coverage and Ranking_Loss, respectively.


Modelling of inquiry diagnosis for coronary heart disease in Traditional Chinese Medicine by using multi-label learning.

Liu GP, Li GZ, Wang YL, Wang YQ - BMC Complement Altern Med (2010)

Results of syndrome models for inquiry diagnosis on whole labels by using ML-kNN and kNN with different k values
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2921356&req=5

Figure 2: Results of syndrome models for inquiry diagnosis on whole labels by using ML-kNN and kNN with different k values
Mentions: In order to determine whether the k value influenced the forecast results in ML-kNN, we construct models with k values as 1, 3, 5, 7, 9 and 11, respectively. Then the forecast results are listed in Figure 2 under the evaluation criteria of Average_Precision, Coverage and Ranking_Loss, respectively, where the horizontal coordinate stands for the k value, and longitudinal coordinate stands for results of Average_Precision, Coverage and Ranking_Loss, respectively.

Bottom Line: In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm.Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively.After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Information Access and Synthesis of TCM Four Diagnosis, Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.

ABSTRACT

Background: Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem.

Methods: Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets.

Results: A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.

Conclusions: The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.

Show MeSH
Related in: MedlinePlus