Data-Influence Analytics in Predictive Models Applied to Asthma Disease

Alejandra Tapia, Viviana Giampaoli, Víctor Leiva and Yuhlong Lio
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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

Mathematics, 2020, vol. 8, issue 9, 1-19

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.

Keywords: binary data; fixed airway obstruction; global and local influence diagnostics; Metropolis–Hastings and Monte Carlo methods; mixed-effects logistic regression; R software (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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