Aprendizaje automático aplicado al análisis del consumo de alcohol y su relación con el estrés percibido

• Pablo Torres-Carrión ^[1] ; Jorge González-Escarabay ^[1] ; Víctor López-Guerra ^[1] ; Silvia Vaca ^[1]
  1. [1] Universidad Técnica Particular de Loja

     Universidad Técnica Particular de Loja

     Loja, Ecuador

     
• Localización: RISTI: Revista Ibérica de Sistemas e Tecnologias de Informação, ISSN-e 1646-9895, Nº. Extra 32, 2020, págs. 483-495
• Idioma: español
• Títulos paralelos:
  • Machine Learning applied to the analysis of alcohol consumption and its relationship with perceived stress
• Enlaces
  • Texto Completo Ejemplar
• Resumen
  • español

    El consumo de alcohol es uno de los problemas que afectan a la población universitaria, en particular en su proceso de aprendizaje y su salud mental. Estudios previos realizados por la OMS y varios científicos a nivel global relacionan el consumo de alcohol con el estrés percibido. Las técnicas de aprendizaje automático se están aplicando a diversos ámbitos de las ciencias para establecer análisis desde una óptica complementaria a la estadística convencional, presentando resultados relevantes para el desarrollo de dichas ciencias. En el marco del Proyecto “CEPRA XII-2018-05 Predicción Consumo de Drogas” se obtuvieron 7134 registros limpios, del test Alcohol Use Disorders Identification Test- Self-Report Version (AUDIT-C) y Perceived Stress scale (PSS-10), junto a datos sociodemográficos de estudiantes universitarios ecuatorianos. Usando la metodología de Lanz (2013), se aplicaron los algoritmos k-means y jerárquico, obteniendo tres agrupaciones relacionadas al consumo de alcohol y el estrés, que corroboran la teoría.

  • English

    Alcohol consumption is one of the problems that affect the university population, particularly in their learning process and mental health. Previous studies carried out by the WHO and several scientists worldwide, link alcohol consumption with perceived stress. Machine learning techniques are being applied to various areas of science to establish analyzes from a complementary perspective to conventional statistics, presenting relevant results for the development of these sciences. In the Project “CEPRA XII-2018-05 Prediction of Drug Use”, 7134 clean records were obtained from the Alcohol Use Disorders Identification TestSelf-Report Version (AUDIT-C) and Perceived stress scale (PSS-10), together to sociodemographic data of Ecuadorian university students. Using the Lanz (2013) methodology, the k-means and hierarchical algorithms were applied, obtaining three groupings related to alcohol consumption and stress, which corroborate the theory.

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