Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/32593
Autoria: Romero, D.
Rubio, L.
Rodrigues, D. L.
Mebarak, M.
Data: 2025
Título próprio: A machine learning approach for analyzing sexual satisfaction based on psychological features
Título da revista: Current Psychology
Volume: 44
Número: 9
Paginação: 7869 - 7878
Referência bibliográfica: Romero, D., Rubio, L., Rodrigues, D. L., & Mebarak, M. (2025). A machine learning approach for analyzing sexual satisfaction based on psychological features. Current Psychology, 44(9), 7869-7878. https://doi.org/10.1007/s12144-024-06813-9
ISSN: 1046-1310
DOI (Digital Object Identifier): 10.1007/s12144-024-06813-9
Palavras-chave: Supervised learning
Sexual satisfaction
Sexual functioning
Personality
Dark triad
Resumo: The emergence of machine learning techniques has revolutionized various fields, helping to shed light into the complexities of human sexuality and address sexuality-related problems. The present study aimed to classify sexual satisfaction in both women (n = 503) and men (n = 342), who completed a digital survey aimed at Colombian adults based on a snowball sampling. Collected data were analyzed using several supervised learning algorithms where inputs included marital status, sociosexuality, sexual drive, sexual functioning, and personality traits. The results showed that the XGBoost model provided best classification results for sexual satisfaction in women, while the Artificial Neural Networks (ANN) had the best performance in classifying sexual satisfaction in men. In both groups, sexual functioning and sexual drive were the most significant predictors of sexual satisfaction. Traits such as extraversion, narcissism, machiavellianism, and sociosexual behavior had a lesser importance. Lastly, psychopathy emerged as a significant predictor of men's sexual satisfaction, whereas conscientiousness emerged as a significant predictor of women's satisfaction. This study provides a technological tool to classify sexual satisfaction using Machine Learning models, in addition, provide, in terms of entropy, variables with the greatest influence based on the data and predictions.
Arbitragem científica: yes
Acesso: Acesso Aberto
Aparece nas coleções:CIS-RI - Artigos em revistas científicas internacionais com arbitragem científica

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