MATEC Web Conf.
Volume 336, 20212020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|Number of page(s)||6|
|Section||Intelligence Algorithms and Application|
|Published online||15 February 2021|
Risk prediction of early diabetes mellitus based on combination model
1 Sichuan Agricultural University, Dujiangyan, China
* Corresponding author: email@example.com
Aiming at the current low pre-diabetes detection rate, this paper proposes a PSO-SVM model to assist doctors in identifying the risk of patients with pre-diabetes. The paper uses the Support Vector Machine as the verification algorithm, takes the radial basis kernel as the kernel function, uses the adaptive Particle Swarm Optimization algorithm to optimize the penalty factor and kernel parameters of the Support Vector Machine, and establishes a PSO-SVM model, finally compares the model with Neural Network, Logistic Regression, and Naive Bayes model, and use Sensitivity, Specificity indicators and ROC curve to evaluate model performance. Empirical analysis proves that the combined model proposed in this paper can effectively identify the risk of patients with prediabetes.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.