Uncovering the Most Important Factors for Predicting Sexual Desire Using Explainable Machine Learning

Laura M. Vowels, Matthew J. Vowels, Kristen P. Mark

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Low sexual desire is the most common sexual problem reported with 34% of women and 15% of men reporting lack of desire for at least 3 months in a 12-month period. Sexual desire has previously been associated with both relationship and individual well-being highlighting the importance of understanding factors that contribute to sexual desire as improving sexual desire difficulties can help improve an individual's overall quality of life. Aim: The purpose of the present study was to identify the most salient individual (eg, attachment style, attitudes toward sexuality, gender) and relational (eg, relationship satisfaction, sexual satisfaction, romantic love) predictors of dyadic and solitary sexual desire from a large number of predictor variables. Methods: Previous research has relied primarily on traditional statistical models which are limited in their ability to estimate a large number of predictors, non-linear associations, and complex interactions. We used a machine learning algorithm, random forest (a type of highly non-linear decision tree), to circumvent these issues to predict dyadic and solitary sexual desire from a large number of predictors across 2 online samples (N = 1,846; includes 754 individuals forming 377 couples). We also used a Shapley value technique to estimate the size and direction of the effect of each predictor variable on the model outcome. Outcomes: The outcomes included total, dyadic, and solitary sexual desire measured using the Sexual Desire Inventory. Results: The models predicted around 40% of variance in dyadic and solitary desire with women's desire being more predictable than men's overall. Several variables consistently predicted dyadic sexual desire such as sexual satisfaction and romantic love, and solitary desire such as masturbation and attitudes toward sexuality. These predictors were similar for both men and women and gender was not an important predictor of sexual desire. Clinical Translation: The results highlight the importance of addressing overall relationship satisfaction when sexual desire difficulties are presented in couples therapy. It is also important to understand clients’ attitudes toward sexuality. Strengths & Limitations: The study improves on existing methodologies in the field and compares a large number of predictors of sexual desire. However, the data were cross-sectional and there may have been variables that are important for desire but were not present in the datasets. Conclusion: Higher sexual satisfaction and feelings of romantic love toward one's partner are important predictors of dyadic sexual desire whereas regular masturbation and more permissive attitudes toward sexuality predicted solitary sexual desire. Vowels LM, Vowels MJ, Mark KP. Uncovering the Most Important Factors for Predicting Sexual Desire Using Explainable Machine Learning. J Sex Med 2021;18:1198–1216.

Original languageEnglish (US)
Pages (from-to)1198-1216
Number of pages19
JournalJournal of Sexual Medicine
Volume18
Issue number7
DOIs
StatePublished - Jul 2021

Bibliographical note

Funding Information:
Funding: This research was supported by the American Institute of Bisexuality and Patty Brisben Foundation for Women's Sexual Health .

Publisher Copyright:
© 2021 International Society for Sexual Medicine

Keywords

  • Close Relationships
  • Machine Learning
  • Random Forests
  • Sexual Desire
  • Shapley Values

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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