|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
Niazi, Muaz A K
|Title:||A new hybrid agent-based modeling & simulation decision support system for breast cancer data analysis|
|Citation:||Siddiqa A, Niazi MAK, Bokhari H, Hussain A, Akram N, Shaheen S, Ahmed F, Iqbal S & Mustafa F (2009) A new hybrid agent-based modeling & simulation decision support system for breast cancer data analysis In: International Conference on Information and Communication Technologies, 2009. ICICT '09.. International Conference on Information and Communication Technologies. IEEE ICICT, IBA, Hoboken, NJ, 15.08.2009-16.08.2009. Hoboken, NJ: Institute of Electrical and Electronics Engineers (IEEE), pp. 134-139. http://icict.iba.edu.pk/icict/icict2009/; https://doi.org/10.1109/ICICT.2009.5267202.|
|Series/Report no.:||International Conference on Information and Communication Technologies|
|Conference Name:||IEEE ICICT, IBA|
|Conference Dates:||2009-08-15 - 2009-08-16|
|Conference Location:||Karachi, Pakistan|
|Abstract:||In this paper, we present a novel technique of building hybrid decision support systems which integrates traditional decision support systems with agent based models for use in breast cancer analysis for better prediction and recommendation. Our system is based on using queries from data (converted to a standardized electronic template) to provide for simulation variables in an agent-based model. The goal is to develop an ICT tool to assist non-specialist biologist researcher users in performing analysis of large amounts of data by applying simple simulation techniques. To demonstrate the effectiveness of this novel decision support system, an extensive breast cancer data collection exercise was carried out with the support of Hospitals in a previously unexplored region. The collected data was subsequently integrated in an electronic medical record filing system for patients. We also demonstrate the application of agent based modeling and simulation techniques for building simulation models of tumor growth and treatment. Our proposed decision support system also provides a comprehensive query tool which facilitates the use of retrieved data in statistical tools for subsequent interpretation and analysis.|
|Status:||VoR - Version of Record|
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