基于临床体征和危险因素预测妊娠期糖尿病的新方法

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2023-01-11 DOI:10.4108/eetsis.v10i3.2697
S. Reddy, Mahesh Gadiraju, N. Preethi, V.V.R.Maheswara Rao, Researc H Article
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引用次数: 4

摘要

妊娠期糖尿病的发生是由于血液中的葡萄糖水平过高。孕妇易患这种糖尿病。要通过验血来确诊糖尿病。口服葡萄糖耐量试验(OGTT)是在妊娠24周至28周之间进行的血液检查,是识别和克服GDM副作用所必需的。本工作的主要目的是利用训练数据训练模型,使用测试数据对训练模型进行评估,并将现有的机器学习算法与梯度增强机(Gradient boosting machine, GBM)进行比较,以获得更好的模型来有效预测妊娠糖尿病。在这项工作中,分析了一些现有的算法和极限学习机和梯度增强技术。采用k-fold交叉验证技术,k值分别为3、5和10,以获得更好的性能。现有实现的算法有朴素贝叶斯分类器、支持向量机、k近邻、ID3、CART和J48。提出了梯度增强和ELM算法。这些算法是用R编程实现的。准确度、kappa统计量、灵敏度/召回率、特异性、精度、f-measure和AUC等指标用于比较所有算法。与现有算法相比,该算法获得了更好的性能。最后,将GBM算法与另一种鲁棒机器学习算法(Extreme Learning Machine)进行比较,结果表明GBM算法具有更好的性能。因此,建议采用梯度增强算法有效预测妊娠期糖尿病。
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A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors
Gestational diabetes mellitus occurs due to high glucose levels in the blood. Pregnant women are affected by this type of diabetes. A blood test is to be performed to identify diabetes. The Oral Glucose Tolerance Test (OGTT) is a blood test performed between the 24th and 28th week of pregnancy that is necessary to identify and overcome the side effects of GDM. The main objective of this work is to train a model by utilizing the training data, evaluate the trained model using the test data, and compare existing machine learning algorithms with a Gradient boosting machine (GBM) to achieve a better model for the effective prediction of gestational diabetes. In this work, the analysis was done with a few existing algorithms and the Extreme learning machine and Gradient boosting techniques. The k-fold cross-validation technique is applied with values of k as 3, 5, and 10 to obtain better performance. The existing algorithms implemented are the Naive Bayes classifier, Support Vector Machine, K-Nearest Neighbour, ID3, CART and J48. The proposed algorithms are Gradient boosting and ELM. These algorithms are implemented in R programming. The metrics like accuracy, kappa statistic, sensitivity/Recall, specificity, precision, f-measure and AUC are used to compare all the algorithms. GBM has obtained better performance than existing algorithms. Then finally, GBM is compared with the other proposed robust Machine Learning algorithm, namely the Extreme learning machine, and the GBM performed better. So, It is recommended to use a gradient-boosting algorithm to predict gestational diabetes effectively.  
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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