Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques

Li, Jun; Chen, Qingguang; Hu, Xiaojuan; Yuan, Pei; Cui, Longtao; Tu, Liping; Cui, Ji; Huang, Jingbin; Jiang, Tao; Ma, Xuxiang; Yao, Xinghua; Zhou, Changle; Lü, Hao; Xu, Jiatuo

doi:10.1016/j.ijmedinf.2021.104429

Cited by 62 publications

(32 citation statements)

References 26 publications

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“…TN represents negative examples that have been correctly labeled as negative, and FN represents positive examples that have been incorrectly labeled as negative. M is the evaluation measure [36]. L is the set of labels.…”

Section: Discussionmentioning

confidence: 99%

Variable Optimization of Seaweed Spectral Response Characteristics and Species Identification in Gouqi Island

Chen

Wang

et al. 2022

Sensors

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Probing the coverage and biomass of seaweed is necessary for achieving the sustainable utilization of nearshore seaweed resources. Remote sensing can realize dynamic monitoring on a large scale and the spectral characteristics of objects are the basis of remote sensing applications. In this paper, we measured the spectral data of six dominant seaweed species in different dry and wet conditions from the intertidal zone of Gouqi Island: Ulva pertusa, Sargassum thunbergii, Chondrus ocellatus, Chondria crassiaulis Harv., Grateloupia filicina C. Ag., and Sargassum fusifarme. The different seaweed spectra were identified and analyzed using a combination of one-way analysis of variance (ANOVA), support vector machines (SVM), and a fusion model comprising extreme gradient boosting (XGBoost) and SVM. In total, 14 common spectral variables were used as input variables, and the input variables were filtered by one-way ANOVA. The samples were divided into a training set (266 samples) and a test set (116 samples) at a ratio of 3:1 for input into the SVM and fusion model. The results showed that when the input variables were the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), Vre, Abe, Rg, Lre, Lg, and Lr and the model parameters were g = 1.30 and c = 2.85, the maximum discrimination rate of the six different wet and dry states of seaweed was 74.99%, and the highest accuracy was 93.94% when distinguishing between the different seaweed phyla (g = 6.85 and c = 2.55). The classification of the fusion model also shows similar results: The overall accuracy is 73.98%, and the mean score of the different seaweed phyla is 97.211%. In this study, the spectral data of intertidal seaweed with different dry and wet states were classified to provide technical support for the monitoring of coastal zones via remote sensing and seaweed resource statistics.

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Section: Discussionmentioning

confidence: 99%

Variable Optimization of Seaweed Spectral Response Characteristics and Species Identification in Gouqi Island

Chen

Wang

et al. 2022

Sensors

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“…TDAS separated the tongue texture and coating, and the tongue features were calculated by the system automatically [18]. Color features of the tongue body and tongue coating employed the component of the RGB, HIS, Lab, and YCrCb color space [19]. Tongue features include tongue texture and tongue coating.…”

Section: Tongue Image Analysis Methodsmentioning

confidence: 99%

A New Method for Identification of Traditional Chinese Medicine Constitutions Based on Data of Tongue features with Machine Learning

Zhao

Duan

Han

et al. 2022

Preprint

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Background: Different Traditional Chinese medicine (TCM) constitution types have different disease susceptibility and tendency, and TCM constitution identification is of great significance in TCM clinical practice. The TCM constitution identification method based on observation and consultation is subjective, and the objective identification technique opens up a new way to modernize TCM treatment. Our study aimed to build a TCM constitution identification model based on tongue feature data and machine learning algorithms, which provides a new fast and accurate method for TCM constitution identification.Methods: We use TFDA-1 tongue diagnostic instrument to collect standardized tongue images of people with Yang deficiency constitution, Yin deficiency constitution and balanced constitution. and use tongue image analysis software (TDAS) to quantitatively analyze tongue color, tongue texture and tongue coating area. Pearson correlation analysis was used to explore the correlation between tongue characteristics and TCM constitution. Four machine learning algorithms, including SVM, decision tree, random forest, and XGboost were used to build a TCM constitution identification model based on tongue features and evaluate the model's effectiveness.Results: The results show that XGboost has the highest accuracy rate among the four machine learning algorithms and the best performance in model evaluation. Pearson correlation analysis found a specific correlation between TCM constitution and tongue features. Significant correlations existed between the Yang deficiency constitution, Yin deficiency constitution, and the balanced constitution with 16 tongue features. In addition, the model's accuracy for the group 2 containing 16 tongue features was higher than that of the whole feature group (Group 1). XGboost was the most effective in this study for identifying TCM constitution, and the tongue features filtered by correlation analysis led to higher accuracy of TCM constitution identification.Conclusions: Tongue feature information can be an essential reference for TCM constitution identification. Machine learning provides a method for rapid identification of TCM constitution types. The XGboost TCM constitution identification model with good performance gives a new way for clinical " Identifying TCM Constitution by Tongue Image" implementation offers a reference and contributes to the performance of " Preventive Treatment of Disease" of TCM and individualized diagnosis and treatment and health preservation. In addition, Objective identification technology has opened up a new way to modernize TCM diagnosis and treatment.

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“…In this case, the 1 × an-1 in An is going to be 0 × an-1, and everything else is going to be the same. Thus, the newly added feedback schema relative to and can be obtained [5].…”

Section: Definitionmentioning

confidence: 99%

Study on Establishment and Improvement Strategy of Aviation Equipment

Zhang

Albeshri

Oudat

2021

Applied Mathematics and Nonlinear Sciences

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As a special commodity with high-tech characteristics required by national development, aviation equipment has the characteristics of confidentiality, long cycle and high risk during the production and research and development. Therefore, the supply chain of its manufacturing and research and development is the focus of national construction and development. On the basis of understanding the current development status of aviation equipment application, this paper analyzes its evaluation system and feedback basic mode through tree vector multiplication and X-0-1 determinant feedback basic mode calculation method, and puts forward corresponding improvement strategies in combination with specific contents, so as to ensure each interest subject to obtain more benefits at the same time. A new management method is proposed for the development of aviation equipment in the future.

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Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques

Cited by 62 publications

References 26 publications

Variable Optimization of Seaweed Spectral Response Characteristics and Species Identification in Gouqi Island

Variable Optimization of Seaweed Spectral Response Characteristics and Species Identification in Gouqi Island

A New Method for Identification of Traditional Chinese Medicine Constitutions Based on Data of Tongue features with Machine Learning

Study on Establishment and Improvement Strategy of Aviation Equipment

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