Conference paper

A MACHINE LEARNING APPROACH TO RELATIONSHIPS AMONG ALEXITHYMIA COMPONENTS

Giovanni Briganti ; Unit of Epidemiology, Biostatistics and Clinical Research, Université libre de Bruxelles Bruxelles, Belgium
Marco Scutari ; IDSIA Dalle Molle Institute for Artificial Intelligence, Lugano, Switzerland
Paul Linkowski ; Unit of Epidemiology, Biostatistics and Clinical Research, Université libre de Bruxelles Bruxelles, Belgium

Abstract

Background: The aim of this paper is to explore the network structures of alexithymia components and compare results with
relevant prior literature.
Subjects and methods: In a large sample of university students, undirected and directed network structures of items from the
Bermond Vorst Alexithymia Questionnaire form B are estimated with state-of-the-art network analysis and structure learning tools.
Centrality estimates are used to address the topic of item redundancy and select relevant alexithymia components to study.
Results: Alexithymia components present positive as well as negative connections; poor fantasy and emotional insight are
identified as central items in the network.
Conclusions: The undirected network structure of alexithymia components reports new features with respect to prior literature,
and the directed network structures offers new insight on the construct.

Keywords

machine learning; alexithymia; Bayesian networks

Hrčak ID:

262514

URI

https://hrcak.srce.hr/262514

Publication date:

24.3.2020.

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