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Authors: Thomas Forstner 1 ; Christiane Dienhart 2 ; Ludmilla Kedenko 2 and Gernot Wolkersdörfer 2

Affiliations: 1 Johannes Kepler University Linz, Austria ; 2 Paracelsus Medical University/Salzburger Landeskliniken, Austria

ISBN: 978-989-758-255-4

Keyword(s): Predictive Models, Insulin Resistance, Logistic Regression, ROC Analysis.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Data Analytics ; Data Engineering ; Data Management and Quality ; Data Modeling and Visualization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Predictive Modeling ; Statistics Exploratory Data Analysis ; Symbolic Systems

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.

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Paper citation in several formats:
Forstner, T.; Dienhart, C.; Kedenko, L. and Wolkersdörfer, G. (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

@conference{data17,
author={Thomas Forstner. and Christiane Dienhart. and Ludmilla Kedenko. and Gernot Wolkersdörfer.},
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},
}

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.
PY - 2017
SP - 121
EP - 125
DO - 10.5220/0006344801210125

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