Department of Computer Science and Information, College of Science Al-Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia.
Biomed Res Int. 2020 Aug 11;2020:3764653. doi: 10.1155/2020/3764653. eCollection 2020.
In this research, the photoplethysmogram (PPG) waveform analysis is utilized to develop a logistic regression-based predictive model for the classification of diabetes. The classifier has three predictors age, /, and SP indices in which they achieved an overall accuracy of 92.3% in the prediction of diabetes. In this study, a total of 587 subjects were enrolled. A total of 459 subjects were used for model training and development, while the rest of the 128 subjects were used for model testing and validation. The classifier was able to diagnose 63 patients correctly as diabetes while 27 subjects were wrongly classified as nondiabetes with an accuracy of 70%. Again, the model classified 479 subjects as nondiabetes correctly while it incorrectly classified 18 subjects as diabetes with an accuracy of 96.4%. Finally, the proposed model revealed an overall predictive accuracy of 92.3% which makes it a reliable surrogate measure for diabetes classification and prediction in clinical settings.
在这项研究中，利用光体积描记图（PPG）波形分析来开发基于逻辑回归的预测模型，以对糖尿病进行分类。该分类器有三个预测因子：年龄、/和 SP 指数，它们在预测糖尿病方面的总体准确率达到 92.3%。在这项研究中，共纳入了 587 名受试者。其中 459 名受试者用于模型训练和开发，其余 128 名受试者用于模型测试和验证。该分类器能够正确诊断 63 名患者为糖尿病，而将 27 名受试者错误地分类为非糖尿病，准确率为 70%。此外，该模型正确地将 479 名受试者分类为非糖尿病，而将 18 名受试者错误地分类为糖尿病，准确率为 96.4%。最后，所提出的模型显示出总体预测准确率为 92.3%，这使其成为临床环境中糖尿病分类和预测的可靠替代指标。
