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How are poor sleepers with other clinical conditions affected by maladaptive personality traits? A neural network-based analysis.
Khazaie, Habibolah; Rezaei, Farzin; Zakiei, Ali; Faridmarandi, Behrooz; Komasi, Saeid.
Afiliación
  • Khazaie H; Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
  • Rezaei F; Department of Psychiatry, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Zakiei A; Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
  • Faridmarandi B; Department of Neuroscience and Psychopathology Research, Mind GPS Institute, Kermanshah, Iran.
  • Komasi S; Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Front Psychiatry ; 15: 1392525, 2024.
Article en En | MEDLINE | ID: mdl-39071228
ABSTRACT

Background:

Psychopathology research mainly focused on the cross-sectional and longitudinal associations between personality and psychiatric disorders without considering the moment-to-moment dynamics of personality in response to environmental situations. The present study aimed to both cluster a young sample according to three mixed clinical conditions (poor sleep quality, depression, and somatization) and to predict the derived clusters by maladaptive personality traits and sex differences using a deep machine learning approach.

Methods:

A sample of 839 adults aged 18-40 years (64% female) from the west of Iran were clustered according to the mixed clinical conditions using the cluster analysis techniques. An Artificial Neural Network (ANN) modeling is used to predict the derived clusters by maladaptive personality traits and biological gender. A receiver operating characteristic (ROC) curve was used to identify independent variables with high sensitivity specific to the derived clusters.

Results:

The cluster analysis techniques suggested a fully stable and acceptable four-cluster solution for Depressed Poor Sleepers, Nonclinical Good Sleepers, Subclinical Poor Sleepers, and Clinical Poor Sleepers. The ANN model led to the identification of one hidden layer with two hidden units. The results of Area under the ROC Curve were relatively to completely acceptable, ranging from.726 to.855. Anhedonia, perceptual dysregulation, depressivity, anxiousness, and unusual beliefs are the most valuable traits with importance higher than 70%.

Conclusion:

The machine learning approach can be well used to predict mixed clinical conditions by maladaptive personality traits. Future research can test the complexity of normal personality traits connected to mixed clinical conditions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Psychiatry Año: 2024 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Psychiatry Año: 2024 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Suiza