摘要
目的/意义探究各种预测模型在2型糖尿病并发动脉粥样硬化风险预测中的应用及其准确率。方法/过程以国家人口健康科学数据中心“糖尿病并发症预警数据集”的生化数据表为基础,运用MATLAB软件,基于最近邻、决策树、反向传播神经网络、朴素贝叶斯模型构建2型糖尿病并发动脉粥样硬化的风险预测模型,并进行比较分析。结果/结论从有效性看,朴素贝叶斯算法的预测准确率最高(61.6%);其次为决策树模型算法(58.2%)、最近邻算法(57.7%)、反向传播神经网络算法(55.9%);混淆矩阵结果与受试者操作特征曲线结果显示,朴素贝叶斯模型表现最好。4种算法构建的预测模型有效性、性能、稳定性等方面均是朴素贝叶斯模型最优。
Purpose/Significance To explore the application and predictive accuracy of various models in predicting the risk of atherosclerosis 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 diabetes-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 model(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.
作者
王一凡
石超君
马晓洁
冯文佳
安洪庆
高倩倩
井淇
蔡伟芹
马安宁
WANG Yifan;SHI Chaojun;MA Xiaojie;FENG Wenjia;AN Hongqing;GAO Qianqian;JING Qi;CAI Weiqin;MA Anning(Shandong Second Medical University,Weifang 261053,China)
出处
《医学信息学杂志》
CAS
2024年第7期74-80,共7页
Journal of Medical Informatics
基金
国家自然科学基金资助项目(项目编号:72104186,72004165)
山东省自然科学基金项目(项目编号:ZR2021MG019)
山东省重点研发(软科学项目)重大项目(项目编号:2020RZB14001)。
