 Diabetes index evaluation framework based on data mining technology: a genetic factor involved solution for predicting diabetes riskYao Wang, Dianhui Chu and Mingqiang Song International Journal of Information Technology and Management, 2019, vol. 18, issue 2/3, 256-267 Abstract: With the development of data mining, scientists began to apply information technology to solve medical problems. In this context, the idea of auxiliary medical service emerged. The purpose of this study is to propose a new framework predicting the probability of suffering from diabetes via diabetes index (DI), which is defined as a score to assess the diabetes-related risk of the participant. DI is calculated based on a diabetic clinical dataset and the SVM model is applied as well. Particularly, genetic feature is innovatively introduced as an important factor in view of the fact that people with family history are more vulnerable to diabetes. The framework is applied to implement a diabetes auxiliary evaluation system. After a set of comprehensive experiments, the assessment result is supposed to identify risk of the disease at an early stage, which contributes to a deeper understanding of one's own health conditions. Keywords: data mining; diabetes evaluation framework; genetic feature; SVM; diabetes auxiliary evaluation system; diabetes index. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijitma:v:18:y:2019:i:2/3:p:256-267 Access Statistics for this article More articles in International Journal of Information Technology and Management from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.