Investigating the impact of age and education on delay discounting: a predictive random forest model for personalized marketing and mental health risk detection

HIGHLIGHTS

• What: In this study, the main effects of age and education on delay discounting rates (AUC) were analyzed to assess their independent influences. The authors utilize a random forest model to predict delay discounting rates and provide personalized recommendations and mental health alerts for individuals with different discounting rates. To ensure the robustness of the model, five-fold cross-validatio was used for further evaluation.
• Who: Huazhang Lu from the University of Electronic Science and Technology of China, Chengdu, China have published the research work: Investigating the Impact of Age and Education on Delay Discounting . . .
  

   

  

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