A tongue features fusion approach to predicting prediabetes and diabetes with machine learning

糖尿病前期 人工智能 机器学习 糖尿病 医学 融合 计算机科学 2型糖尿病 内分泌学 语言学 哲学
作者
Jun Li,Pei Yuan,Xiaojuan Hu,Jing-bin Huang,Longtao Cui,Ji Cui,Xuxiang Ma,Tao Jiang,Xinghua Yao,Jiacai Li,Yulin Shi,Zijuan Bi,Yu Wang,FU Hong-yuan,Jue Wang,Yen-Ting Lin,Ching-Hsuan Pai,Xiaojing Guo,Changle Zhou,Liping Tu
出处
期刊:Journal of Biomedical Informatics [Elsevier BV]
卷期号:115: 103693-103693 被引量:91
标识
DOI:10.1016/j.jbi.2021.103693
摘要

Diabetics has become a serious public health burden in China. Multiple complications appear with the progression of diabetics pose a serious threat to the quality of human life and health. We can prevent the progression of prediabetics to diabetics and delay the progression to diabetics by early identification of diabetics and prediabetics and timely intervention, which have positive significance for improving public health.Using machine learning techniques, we establish the noninvasive diabetics risk prediction model based on tongue features fusion and predict the risk of prediabetics and diabetics.Applying the type TFDA-1 Tongue Diagnosis Instrument, we collect tongue images, extract tongue features including color and texture features using TDAS, and extract the advanced tongue features with ResNet-50, achieve the fusion of the two features with GA_XGBT, finally establish the noninvasive diabetics risk prediction model and evaluate the performance of testing effectiveness.Cross-validation suggests the best performance of GA_XGBT model with fusion features, whose average CA is 0.821, the average AUROC is 0.924, the average AUPRC is 0.856, the average Precision is 0.834, the average Recall is 0.822, the average F1-score is 0.813. Test set suggests the best testing performance of GA_XGBT model, whose average CA is 0.81, the average AUROC is 0.918, the average AUPRC is 0.839, the average Precision is 0.821, the average Recall is 0.81, the average F1-score is 0.796. When we test prediabetics with GA_XGBT model, we find that the AUROC is 0.914, the Precision is 0.69, the Recall is 0.952, the F1-score is 0.8. When we test diabetics with GA_XGBT model, we find that the AUROC is 0.984, the Precision is 0.929, the Recall is 0.951, the F1-score is 0.94.Based on tongue features, the study uses classical machine learning algorithm and deep learning algorithm to maximum the respective advantages. We combine the prior knowledge and potential features together, establish the noninvasive diabetics risk prediction model with features fusion algorithm, and detect prediabetics and diabetics noninvasively. Our study presents a feasible method for establishing the association between diabetics and the tongue image information and prove that tongue image information is a potential marker which facilitates effective early diagnosis of prediabetics and diabetics.
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