Comparison of Risk Prediction Models for Atherosclerosis in Type 2 Diabetes Mellitus
10.3969/j.issn.1673-6036.2024.07.013
- VernacularTitle:2型糖尿病并发动脉粥样硬化风险预测模型比较
- Author:
Yifan WANG
1
;
Chaojun SHI
;
Xiaojie MA
;
Wenjia FENG
;
Hongqing AN
;
Qianqian GAO
;
Qi JING
;
Weiqin CAI
;
Anning MA
Author Information
1. 山东第二医科大学 潍坊 261053
- Keywords:
type 2 diabetes complications;
atherosclerosis;
disease prediction model
- From:
Journal of Medical Informatics
2024;45(7):74-80
- CountryChina
- Language:Chinese
-
Abstract:
Purpose/Significance To explore the application and predictive accuracy of various models in predicting the risk of ather-osclerosis in diabetic patients.Method/Process Based on the biochemical data table from the"Diabetes Complications Warning Dataset"provided by the National Population Health Science Data Center,MATLAB software is used to construct risk prediction models for diabe-tes-induced atherosclerosis.The models are built by using k-nearest neighbors(KNN),decision trees,backpropagation(BP)neural networks,and Naive Bayes algorithms,and which are subjected to comparative analysis.Result/Conclusion In terms of effectiveness,the predictive accuracy of Naive Bayes algorithm is the highest(61.6％),followed by the decision tree model(58.2％),the KNN mod-el(57.7％),and the BP neural network model(55.9％).The results of the confusion matrix and the receiver operating characteristic(ROC)curve indicate that the Naive Bayes model performs best.When comparing the models in terms of effectiveness,performance and stability,the Naive Bayes model is superior.
