Abstract
In the era of big data, recent developments in the field of information and communication technologies are facilitating organizations to innovate and grow. These technological developments and wide adaptation of ubiquitous computing enable numerous opportunities for government and companies to discover useful trends or patterns that are used in health-care decision making. A common problem affecting data quality is the presence of noise and irrelevant information which can lead decision makers to a wrong decision. Intelligent Decision Support System (IDSS) an automated judgment that supports decision making is composed of human and computer interaction to help in decision-making accuracy. Also, multi-agent systems (MAS) are collections of independent intelligent entities that collaborate in the joint resolution of a complex problem. Multi-agent IDSS can be used to solve large-scale convention problem. In this paper, we introduce a multiagent-MapReduce framework based dimension reduction for medical diagnosis that can filter the noise and irrelevant information and keeps only smart data, which can lead to a reduced storage space in one hand and produce a better health-care decision in the other hand.
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Elaggoune, Z., Maamri, R., Boussebough, I. (2018). A Multi-agent Framework for Medical Diagnosis Driven Smart Data in a Big Data Environment. In: Auer, M., Tsiatsos, T. (eds) Interactive Mobile Communication Technologies and Learning. IMCL 2017. Advances in Intelligent Systems and Computing, vol 725. Springer, Cham. https://doi.org/10.1007/978-3-319-75175-7_71
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DOI: https://doi.org/10.1007/978-3-319-75175-7_71
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