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Discovering Associative Patterns in Healthcare Data

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Proceedings of Sixth International Congress on Information and Communication Technology

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 235))

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Abstract

Health care has several knowledge discovery techniques. Among them are association rules, which provide quick access to standards. However, classic algorithms can generate many patterns or fail to identify rare cases relevant to healthcare professionals. This study identified asymmetric associative patterns in health-related data using the Health Association Rules (HAR) algorithm. We use a combined strategy of six metrics to filter, select, and eliminate contradiction steps to find patterns and identify possible rare cases. The proposed solution uses adjustment mechanisms to increase the quality of standards with knowledge of the health professional. The HAR assists health researchers and decision support systems. A survey of 597 studies identified the primary needs and problems of associative patterns in the health context. The HAR identifies characteristics with the highest cause and effect relationship. The experiments were carried out on 13 datasets, where we identified the most pertinent patterns for the datasets without losing relevant knowledge.

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Author information

Authors and Affiliations

  1. Federal Institute of Tocantins, Palmas, Brazil

    Diego de Castro Rodrigues, Vilson Siqueira & Fabiano Tavares

  2. Federal University of Goiás - UFG, Goiânia, Brazil

    Diego de Castro Rodrigues, Vilson Siqueira, Márcio Lima, Frederico Oliveira, Ronaldo Costa & Rommel Barbosa

  3. University of Western São Paulo - UNOESTE, São Paulo, Brazil

    Lucas Osco & Wilmar Junior

Authors
  1. Diego de Castro Rodrigues
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  2. Vilson Siqueira
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  3. Fabiano Tavares
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  4. Márcio Lima
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  5. Frederico Oliveira
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  6. Lucas Osco
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  7. Wilmar Junior
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  8. Ronaldo Costa
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  9. Rommel Barbosa
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Corresponding author

Correspondence to Diego de Castro Rodrigues .

Editor information

Editors and Affiliations

  1. Middlesex University, London, UK

    Xin-She Yang

  2. University of Reading, Reading, UK

    Simon Sherratt

  3. JIS University, Kolkata, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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de Castro Rodrigues, D. et al. (2022). Discovering Associative Patterns in Healthcare Data. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_35

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