Comparison of risk prediction models for atherosclerosis in type 2 diabetes mellitus

Corresponding author: maanning, yxyman@126.com

DOI: 10.12201/bmr.202404.00007

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  Abstract: Through the analysis of the biochemical data table of the Diabetes Complications Early Warning Dataset from the National Population Health Science Data Center, the application of various predictive models in predicting the risk of diabetes complicating atherosclerosis and their prediction accuracy is explored. MATLAB software is used to construct risk prediction models for diabetes complicating atherosclerosis based on KNN, decision tree, BP neural network, and naive Bayes models, and the performance of the models is compared using corresponding evaluation metrics. Through model comparison, it is found that in terms of effectiveness, the naive Bayes algorithm has the highest prediction accuracy (61.6%); followed by the decision tree model algorithm (58.2%), KNN model algorithm (57.7%), and BP neural network algorithm (55.9%); confusion matrix results and ROC curve results show that the naive Bayes model performs the best among the four models. Comparing the predictive models constructed by the four algorithms in terms of effectiveness, performance, stability, etc., the naive Bayes model is optimal in all aspects.

  Key words: typeⅡdiabetes; mellitus complications; predictive models

  Submit time: 10 April 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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