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Decision Support Based on Bio-PEPA Modeling and Decision Tree Induction: A New Approach, Applied to a Tuberculosis Case Study

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.

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.

Keywords
Decision support; decision tree induction; data mining; Bio-PEPA modelling; modelling and simulation; tuberculosis; epidemiology; refinement; optimisation

StatusPublished
AuthorsHamami Dalila, Baghdad Atmani, Shankland Carron
Publication date2017
Date accepted by journal18/04/2016
PublisherIGI Global for Information Resources Management Association
ISSN 1935-5688
LanguageEnglish

Journal
International Journal of Information Systems in the Service Sector: Volume 9, Issue 2

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