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Association Rule Mining for Multifactorial Diseases: Survey and Opportunities

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Management of Digital EcoSystems (MEDES 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2022))

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Abstract

Association Rule Mining is an efficient Data Mining task that has been largely used in health informatics research since its emergence. As health informatics and in particular multifactorial diseases have received a lot of attention from researchers in the last decade, and have shown its great importance, it is therefore worth considering the state of the art of multifactorial diseases research. Since researchers and knowledge discovery experts have implemented a set of Data Mining techniques for knowledge extraction from health data, the application of Association Rule Mining techniques for multifactorial diseases has been condensed and investigated in detail in this study. The limitations related to the applications of Association Rule Mining for the discovery of factors responsible for multifactorial diseases were highlighted and recommendations were gived to address these limitations. In addition, algorithms and tools employed for the application of Association Rule Mining were also specified, conclusions were learned from the reviewed literature, and future research guidelines were provided.

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Authors and Affiliations

  1. Laboratory of Technologies of Information and Communication and Electrical Engineering (LaTICE), National Higher School of Engineers of Tunis (ENSIT), University of Tunis, Tunis, Tunisia

    Hend Amraoui & Faouzi Mhamdi

  2. Faculty of Mathematical, Physical and Natural Sciences of Tunis, University of Tunis El Manar, Tunis, Tunisia

    Hend Amraoui

  3. Higher Institute of Applied Languages and Computer Science of Beja, University of Jendouba, Jendouba, Tunisia

    Faouzi Mhamdi

Authors
  1. Hend Amraoui
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  2. Faouzi Mhamdi
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Correspondence to Faouzi Mhamdi .

Editor information

Editors and Affiliations

  1. University of Pau & Pays de l'Adour, Anglet, France

    Richard Chbeir

  2. Claude Bernard University Lyon 1, Villeurbanne Cedex, France

    Djamal Benslimane

  3. Technical University of Crete, Chania, Greece

    Michalis Zervakis

  4. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  5. Wroclaw University of Science, Wroclaw, Poland

    Ngoc Thanh Ngyuen

  6. Lebanese American University, Byblos, Lebanon

    Joe Tekli

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Amraoui, H., Mhamdi, F. (2024). Association Rule Mining for Multifactorial Diseases: Survey and Opportunities. In: Chbeir, R., Benslimane, D., Zervakis, M., Manolopoulos, Y., Ngyuen, N.T., Tekli, J. (eds) Management of Digital EcoSystems. MEDES 2023. Communications in Computer and Information Science, vol 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-51643-6_12

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  • DOI: https://doi.org/10.1007/978-3-031-51643-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51642-9

  • Online ISBN: 978-3-031-51643-6

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