Data-influence analytics in predictive models applied to asthma disease
Autor
Tapia, Alejandra
Giampaoli, Viviana
Leiva, Víctor
Lio, Yuhlong
Fecha
2020Resumen
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.
Fuente
Mathematics, 8(9), 1587Link de Acceso
Click aquí para ver el documentoIdentificador DOI
doi.org/10.3390/math8091587Colecciones
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