The prediction of diabetes development: A machine learning framework

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

Producción científica: Conference contribution

3 Citas (Scopus)

Resumen

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%.

Idioma originalEnglish (US)
Título de la publicación alojada2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering, MECBME 2020
EditorialIEEE Computer Society
ISBN (versión digital)9781728123585
DOI
EstadoPublished - oct 27 2020
Evento5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020 - Amman, Jordan
Duración: oct 27 2020oct 29 2020

Serie de la publicación

NombreMiddle East Conference on Biomedical Engineering, MECBME
Volumen2020-October
ISSN (versión impresa)2165-4247
ISSN (versión digital)2165-4255

Conference

Conference5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020
País/TerritorioJordan
CiudadAmman
Período10/27/2010/29/20

ASJC Scopus subject areas

  • Biomedical Engineering

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