Verifying the significance of arterial injury for early detection of diabetes by Stacking ensemble learning algorithm(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第8期
- Page:
- 1003-1009
- Research Field:
- 医学信号处理与医学仪器
- Publishing date:
Info
- Title:
- Verifying the significance of arterial injury for early detection of diabetes by Stacking ensemble learning algorithm
- Author(s):
- ZHANG Mingwei1; 2; ZHANG Tianyi1; 2; ZHONG Ming3; CHENG Yunzhang1; 2
- 1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Shanghai Interventional Medical Device Engineering Technology Research Center, Shanghai 200093, China 3. Zhongshan Hospital, Fudan University, Shanghai 200032, China
- Keywords:
- Keywords: diabetes pulse signal wavelet decomposition ensemble algorithm arterial injury
- PACS:
- R318；R587.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.08.015
- Abstract:
- Abstract: Background Diabetes can cause extensive pathological changes in the structure and function of arteries, leading to increased arterial stiffness, decreased compliance, and decreased arterial elasticity. From the perspective of arterial injury, this study aims to realize the early detection of diabetes in patients who have not yet appeared clinical manifestations of diabetes but have arterial injury. Methods Arterial injury leads to mechanical parameters changes in the vascular system. The waveform changes of pulse signals are closely related to mechanical parameters changes in the cardiovascular system. By decomposing the pulse signals of diabetic patients with 9-level wavelet, cD8, cD7 and cD6 coefficients (medium-high frequency components, representing the features in signal details) were extracted as features that reflect the degree of arterial injury. The feature matrix was input into the Stacking ensemble learning algorithm of the 10-fold cross-validation model, with SVM, Random Forest, XGBoost and Extra Trees as the 4 base-learners of the first layer, and KNN as the meta-learner of the second layer. Results A single machine learning model could achieve an accuracy higher than 90%. Stacking ensemble learning algorithm was 4%-5% higher than a single machine learning model in accuracy, and 1%-6% higher in area under the ROC curve (AUC). Conclusion The cD8, cD7, and cD6 coefficients of pulse signals obtained by wavelet decomposition can effectively reflect the degree of arterial injury caused by diabetes. Therefore, arterial injury has certain guiding significance for the early detection of diabetes. Stacking ensemble learning algorithm that combines the advantages of multiple models to generate a new model can achieve better performance than single models.
Last Update: 2022-09-05