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 . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.