|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Decision Support Based on Bio-PEPA Modeling and Decision Tree Induction: A New Approach, Applied to a Tuberculosis Case Study|
decision tree induction
modelling and simulation
|Citation:||Hamami D, Baghdad A & Shankland C (2017) Decision Support Based on Bio-PEPA Modeling and Decision Tree Induction: A New Approach, Applied to a Tuberculosis Case Study. International Journal of Information Systems in the Service Sector, 9 (2), pp. 71-101. https://doi.org/10.4018/IJISSS.2017040104.|
|Abstract:||The problem of selecting determinant features generating appropriate model structure is a challenge in epidemiological modelling. Disease spread is highly complex, and experts develop their understanding of its dynamic over years. There is an increasing variety and volume of epidemiological data which adds to the potential confusion. We propose here to make use of that data to better understand disease systems. Decision tree techniques have been extensively used to extract pertinent information and improve decision making. In this paper, we propose an innovative structured approach combining decision tree induction with Bio-PEPA computational modelling, and illustrate the approach through application to tuberculosis. By using decision tree induction, the enhanced Bio-PEPA model shows considerable improvement over the initial model with regard to the simulated results matching observed data. The key finding is that the developer expresses a realistic predictive model using relevant features, thus considering this approach as decision support, empowers the epidemiologist in his policy decision making.|
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[shankland IJSSS.pdf] The publisher has granted permission for use of this work in this Repository. Published in International Journal of Information Systems in the Service Sector, Volume 9, Issue 2, April-June 2017, pp. 71-101: https://doi.org/10.1634/theoncologist.2016-0371
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