Using supervised machine learning and evolutionary optimization to understand risk factors of dissociation in young people

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• Daniel Herring (University of Birmingham)
• Monday 16 October 2023, 14:00-15:00
• Room 104, Arts Building (R16).

If you have a question about this talk, please contact Xiaocheng Shang.

Dissociation is a condition that disproportionately impacts young adults and impacts their daily lives; the mildest experiences are akin to ‘zoning out’ but more severe cases can include the feeling of detachment from reality, depersonalization (where your thoughts and feelings don’t belong to you) as well as identity confusion and amnesia. The causes and contributing factors for dissociation are not well understood and can vary, however the recently proposed ČEFSA-14 score allows the quantification of Felt Sense of Anomaly (FSA) subtype of dissociative experiences and thus for novel analyses. A dataset comprising 2384 responses from the general UK population including demographic information and psychological measurements including trauma, stress and anxiety, allows for analysis towards a multifactorial explanation of increased risk for dissociation. The application of a Naïve Bayes classifier (NBC) testing framework allows for the relative importance of different key predictors (measurements) to be ascertained. Evolutionary optimization is then used to determine the optimal subset from a wider range of predictors maximises the NBC classification accuracy and therefore highlights the most important factors influencing dissociation. Analysis on data partitioned by demographic information and the construction of high-risk profiles is undertaken to allow for meaningful clinical impact and actionable outcomes to arise from this analysis.

This talk is part of the Data Science and Computational Statistics Seminar series.

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• Data Science and Computational Statistics Seminar
• Room 104, Arts Building (R16)
• School of Mathematics events

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