Predicting Diabetes in Healthy Population through Machine Learning

被引：8

作者：

Alic, Lejla ^{[1
]}

Abbas, Hasan T. ^{[2
,4
]}

Rios, Marelyn ^{[3
]}

AbdulGhani, Muhammad ^{[5
]}

Qaraqe, Khalid ^{[2
]}

机构：

[1] Univ Twente, Fac Sci & Technol, Magnet Detect & Imaging Grp, Enschede, Netherlands

[2] Texas A&M Univ Qatar, Dept Elect & Comp Engn, Doha 23874, Qatar

[3] Texas A&M Univ, Dept Ind & Syst Engn, College Stn, TX 77843 USA

[4] Univ Glasgow, Elect & Nanoscale Engn, Glasgow G12 8QQ, Lanark, Scotland

[5] Univ Texas Hlth Sci Ctr San Antonio, Div Diabet, San Antonio, TX 78229 USA

来源：

2019 IEEE 32ND INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS) | 2019年

关键词：

Disease Prediction; support vector machine; type; 2; diabetes; IMPAIRED GLUCOSE-TOLERANCE; GLYCEMIA; RISK;

D O I：

10.1109/CBMS.2019.00117

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we revisit the data of the San Antonio Heart Study, and employ machine learning to predict the future development of type-2 diabetes. To build the prediction model, we use the support vector machines and ten features that are well-known in the literature as strong predictors of future diabetes. Due to the unbalanced nature of the dataset in terms of the class labels, we use 10-fold cross-validation to train the model and a hold-out set to validate it. The results of this study show a validation accuracy of 84.1% with a recall rate of 81.1% averaged over 100 iterations. The outcomes of this study can help in identifying the population that is at high risk of developing type-2 diabetes in the future.

引用

页码：567 / 570

页数：4

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