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Data-Influence Analytics in Predictive Models Applied to Asthma Disease

Author

Listed:
  • Alejandra Tapia

    (Faculty of Basic Sciences, Universidad Católica del Maule, Talca 3466706, Chile)

  • Viviana Giampaoli

    (Institute of Mathematics and Statistics, Universidade de São Paulo, São Paulo 01000-000, Brazil)

  • Víctor Leiva

    (School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile)

  • Yuhlong Lio

    (Department of Mathematical Sciences, University of South Dakota, Vermillion, SD 57069, USA)

Abstract

Asthma is one of the most common chronic diseases around the world and represents a serious problem in human health. Predictive models have become important in medical sciences because they provide valuable information for data-driven decision-making. In this work, a methodology of data-influence analytics based on mixed-effects logistic regression models is proposed for detecting potentially influential observations which can affect the quality of these models. Global and local influence diagnostic techniques are used simultaneously in this detection, which are often used separately. In addition, predictive performance measures are considered for this analytics. A study with children and adolescent asthma real data, collected from a public hospital of São Paulo, Brazil, is conducted to illustrate the proposed methodology. The results show that the influence diagnostic methodology is helpful for obtaining an accurate predictive model that provides scientific evidence when data-driven medical decision-making.

Suggested Citation

  • Alejandra Tapia & Viviana Giampaoli & Víctor Leiva & Yuhlong Lio, 2020. "Data-Influence Analytics in Predictive Models Applied to Asthma Disease," Mathematics, MDPI, vol. 8(9), pages 1-19, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1587-:d:413734
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    References listed on IDEAS

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