Models for Predicting the Development of Insulin Resistance

Thomas Forstner, Christiane Dienhart, Ludmilla Kedenko, Gernot Wolkersdörfer, Bernhard Paulweber

2017

Abstract

Insulin resistance is the leading cause for developing type 2 diabetes. Early determination of insulin resistance and herewith of impending type 2 diabetes could help to establish sooner preventive measures or even therapies. However, an optimal predictive model for developing insulin resistance has not been established yet. Based on the data of an Austrian cohort study (SAPHIR study) various predictive models were calculated and compared to each other. For developing predictive models logistic regression models were used. For finding an optimal cut-off value an ROC approach was used. Based on various biochemical parameters an overall percentage of around 82% correct classifications could be achieved.

Paper Citation

in Harvard Style

Forstner T., Dienhart C., Kedenko L., Wolkersdörfer G. and Paulweber B. (2017). Models for Predicting the Development of Insulin Resistance.In Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-255-4, pages 121-125. DOI: 10.5220/0006344801210125

in Bibtex Style

@conference{data17,
author={Thomas Forstner and Christiane Dienhart and Ludmilla Kedenko and Gernot Wolkersdörfer and Bernhard Paulweber},
title={Models for Predicting the Development of Insulin Resistance},
booktitle={Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2017},
pages={121-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006344801210125},
isbn={978-989-758-255-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Models for Predicting the Development of Insulin Resistance
SN - 978-989-758-255-4
AU - Forstner T.
AU - Dienhart C.
AU - Kedenko L.
AU - Wolkersdörfer G.
AU - Paulweber B.
PY - 2017
SP - 121
EP - 125
DO - 10.5220/0006344801210125