A Microcosmic Syndrome Differentiation Model for Metabolic Syndrome with Multilabel Learning.

Shujie Xia,Shaozi Li,Bizhen Gao,Guodong Du,Candong Li,Zhaoyang Yang,Jia Zhang,Jiliang Xin,Long Zhu,Chi Teng Vong,Kanwal Rehman

doi:10.1155/2020/9081641

Abstract

Background Metabolic syndrome (MS) is a complex multisystem disease. Traditional Chinese medicine (TCM) is effective in preventing and treating MS. Syndrome differentiation is the basis of TCM treatment, which is composed of location and/or nature syndrome elements. At present, there are still some problems for objective and comprehensive syndrome differentiation in MS. This study mainly proposes a solution to two problems. Firstly, TCM syndromes are concurrent, that is, multiple TCM syndromes may develop in the same patient. Secondly, there is a lack of holistic exploration of the relationship between microscopic indexes, and TCM syndromes. In regard to these two problems, multilabel learning (MLL) method in machine learning can be used to solve them, and a microcosmic syndrome differentiation model can also be built innovatively, which can provide a foundation for the establishment of the next model of multidimensional syndrome differentiation in MS. Methods The standardization scale of TCM four diagnostic information for MS was designed, which was used to obtain the results of TCM diagnosis. The model of microcosmic syndrome differentiation was constructed based on 39 physicochemical indexes by MLL techniques, called ML-kNN. Firstly, the multilabel learning method was compared with three commonly used single learning algorithms. Then, the results from ML-kNN were compared between physicochemical indexes and TCM information. Finally, the influence of the parameter k on the diagnostic model was investigated and the best k value was chosen for TCM diagnosis. Results A total of 698 cases were collected for the modeling of the microcosmic diagnosis of MS. The comprehensive performance of the ML-kNN model worked obviously better than the others, where the average precision of diagnosis was 71.4%. The results from ML-kNN based on physicochemical indexes were similar to the results based on TCM information. On the other hand, the k value had less influence on the prediction results from ML-kNN. Conclusions In the present study, the microcosmic syndrome differentiation model of MS with MLL techniques was good at predicting syndrome elements and could be used to solve the diagnosis problems of multiple labels. Besides, it was suggested that there was a complex correlation between TCM syndrome elements and physicochemical indexes, which worth future investigation to promote the development of objective differentiation of MS.

Highlights

Metabolic syndrome (MS) is a metabolic disorder syndrome, which was characterized by obesity, hyperglycemia, hypertension, dyslipidemia, and hyperuricemia, and this seriously endangers the health of patients [1]
According to the “syndrome differentiation of 600 common symptoms” and the standards of the MS common symptoms in the Guidelines of Clinical Research of Traditional Chinese medicine (TCM) New Drugs, the four diagnosis information collection scale was established. e symptoms and signs of disease were classified as none, mild, moderate, and severe with 0, 1, 2, and 3 points, respectively. e four diagnostic data were collected by two qualified professionals of TCM. e physicochemical indexes included the following indexes: body weight, height, blood pressure, abdominal circumference, blood routine, fasting blood sugar, insulin, blood lipid, liver function, and kidney function
TCM is an empirical medicine, and the discovery of the underlying diagnostic rules can contribute to the development of Chinese medicine. e rules often contain a large number of information on TCM diagnoses and treatment; the way of how to extract accurate and potential rules from considerable cases becomes an important strategy in the field of TCM

Summary

Introduction

Metabolic syndrome (MS) is a metabolic disorder syndrome, which was characterized by obesity, hyperglycemia, hypertension, dyslipidemia, and hyperuricemia, and this seriously endangers the health of patients [1]. There is a lack of holistic exploration of the relationship between microscopic indexes, and TCM syndromes In regard to these two problems, multilabel learning (MLL) method in machine learning can be used to solve them, and a microcosmic syndrome differentiation model can be built innovatively, which can provide a foundation for the establishment of the model of multidimensional syndrome differentiation in MS. The microcosmic syndrome differentiation model of MS with MLL techniques was good at predicting syndrome elements and could be used to solve the diagnosis problems of multiple labels. It was suggested that there was a complex correlation between TCM syndrome elements and physicochemical indexes, which worth future investigation to promote the development of objective differentiation of MS

Methods

Results

Discussion

Conclusion