Data-influence analytics in predictive models applied to asthma disease

Alejandra Tapia, Viviana Giampaoli, VICTOR ELISEO LEIVA SANCHEZ, Yuhlong Lio

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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 Sao 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.

Original languageEnglish
Article number1587
JournalMathematics
Volume8
Issue number9
DOIs
StatePublished - Sep 2020
Externally publishedYes

Keywords

  • Binary data
  • Fixed airway obstruction
  • Global and local influence diagnostics
  • Metropolis-hastings and monte carlo methods
  • Mixed-effects logistic regression
  • R software

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