The prediction of diabetes development: A machine learning framework

Md Shafiqul Islam, Marwa K. Qaraqe, Hasan T. Abbas, Madhav Erraguntla, Muhammad Abdul-Ghani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The development of diabetes occurs due to elevated glucose levels in the bloodstream. Prevention of diabetes or the delayed onset of diabetes is crucial. It can be achieved if there exists a screening process that can accurately identify individuals who are at a higher risk of developing diabetes in the future. Although there are many works employing machine learning techniques in medical diagnostics, there is little work done regarding the long term prediction of disease, type 2 diabetes in particular. In this study, we propose a machine learning framework consists of finding the best features that are highly correlated with the future development of diabetes, followed by developing diabetes prediction models. The proposed models are evaluated using data from a longitudinal clinical study known as the San Antonio Heart Study. Our approach has managed to achieve a long-term prediction accuracy of 81.01%, a specificity of 81.2%, a sensitivity of 79.5%, and an AUC score of 87.1%.

Original languageEnglish (US)
Title of host publication2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering, MECBME 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728123585
DOIs
StatePublished - Oct 27 2020
Event5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020 - Amman, Jordan
Duration: Oct 27 2020Oct 29 2020

Publication series

NameMiddle East Conference on Biomedical Engineering, MECBME
Volume2020-October
ISSN (Print)2165-4247
ISSN (Electronic)2165-4255

Conference

Conference5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020
Country/TerritoryJordan
CityAmman
Period10/27/2010/29/20

Keywords

  • Diabetes Prediction
  • Ensemble of Classifiers
  • Feature Selection
  • Machine Learning

ASJC Scopus subject areas

  • Biomedical Engineering

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