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Keywords = multidimensional depression diagnostic index

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21 pages, 2406 KB  
Article
Determining Factors for the Diagnosis of Multidimensional Depression and Its Representation: A Composite Indicator Based on Linear Discriminant Analysis
by Matheus Pereira Libório, Angélica C. G. Santos, Marcos Flávio Silveira Vasconcelos D’angelo, Hasheem Mannan, Cristiane Neri Nobre, Ariane Carla Barbosa da Silva, Petr Iakovlevitch Ekel and Allysson Steve Mota Lacerda
Appl. Sci. 2025, 15(15), 8275; https://doi.org/10.3390/app15158275 - 25 Jul 2025
Cited by 1 | Viewed by 639
Abstract
This study proposes a novel approach to constructing composite indicators, utilizing discriminant analysis to identify the determining factors for the diagnosis of multidimensional depression and to provide an index that represents the multidimensionality of this construct. By focusing solely on factors that significantly [...] Read more.
This study proposes a novel approach to constructing composite indicators, utilizing discriminant analysis to identify the determining factors for the diagnosis of multidimensional depression and to provide an index that represents the multidimensionality of this construct. By focusing solely on factors that significantly correlate with the diagnosis of multidimensional depression, this method provides a more precise and objective representation of the problem. The application of the method to the 2019 Brazilian Health Survey data demonstrated its effectiveness, resulting in a composite indicator that separates individuals who self-declare as having depression from individuals who self-declare as not having depression. The results highlight individuals who have a limiting chronic condition, high levels of education, less support from friends and family, perform unhealthy work, and are male. In contrast, individuals with the opposite characteristics are associated with a negative multidimensional depression diagnosis. The proposed composite indicator not only improves the measurement accuracy but also offers a new means of visualizing and understanding the multidimensional nature of depression diagnosis, providing valuable information for the formulation of targeted public health policies aimed at reducing the time for which people show symptoms of depression. Full article
(This article belongs to the Special Issue State-of-the-Art of Intelligent Decision Support Systems)
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