A tongue features fusion approach to predicting prediabetes and diabetes with machine learning
A tongue features fusion approach to predicting prediabetes and diabetes with machine learning
复制标题
舌头采用融合方法通过机器学习预测糖尿病前期和糖尿病
DOI：
10.1016/j.jbi.2021.103693
复制
发表时间：
2021-02-16
影响因子：
4.5
通讯作者：
Xu, Jiatuo
中科院分区：
文献类型：
--
作者：
Li, Jun;Yuan, Pei;Xu, Jiatuo
背景：糖尿病已成为我国严重的公共卫生负担。随着糖尿病病情的发展，出现多种并发症，严重威胁着人类的生活质量和健康。通过对糖尿病患者及糖尿病前期的早期识别和及时干预，可以阻止糖尿病前期向糖尿病的进展，延缓向糖尿病的进展，对改善公共卫生具有积极意义。目的：利用机器学习技术，建立基于舌象融合的无创糖尿病风险预测模型，预测糖尿病前期和糖尿病患者的风险。研究方法：应用TFDA-1型舌象诊断仪采集舌象，利用TDAS提取舌象的颜色和纹理特征，利用ResNet 50提取高级舌象特征，利用GA_XGBT实现两种特征的融合，最终建立无创糖尿病风险预测模型，并对检测效果进行评价。结果如下：交叉验证结果表明，融合特征的GA_XGBT模型性能最好，其平均CA为0.821，平均AUROC为0.924，平均AUPRC为0.856，平均精确度为0.834，平均召回率为0.822，平均F1得分为0.813。测试集表明GA_XGBT模型的测试性能最好，其平均CA为0.81，平均AUROC为0.918，平均AUPRC为0.839，平均精确度为0.821，平均召回率为0.81，平均F1分数为0.796。当我们用GA_XGBT模型对糖尿病前期患者进行检验时，我们发现AUROC为0.914，精确度为0.69，召回率为0.952，F1评分为0.8。当我们用GA_XGBT模型对糖尿病患者进行测试时，我们发现AUROC为0.984，精确度为0.929，召回率为0.951，F1得分为0.94。结论：基于舌象特征，采用经典的机器学习算法和深度学习算法，最大限度地发挥各自的优势。将先验知识和潜在特征相结合，采用特征融合算法建立无创性糖尿病风险预测模型，对糖尿病前期和糖尿病患者进行无创性检测。本研究为糖尿病患者舌象信息的建立提供了一种可行的方法，并证明舌象信息是一种潜在的标记物，有助于糖尿病前期和糖尿病患者的有效早期诊断。
Background: 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. Objective: 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. Methods: 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 ResNet50, 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. Results: 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. Conclusions: 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.