Machine Learning Studies on Dementia Risk for Ethnoracial Minorities
Author Information
Author(s): Hong Michin, Ji Soo-Yeon, Kim Seon, Kim Kyeongmo
Primary Institution: Indiana University Indianapolis
Hypothesis
The study aims to review dementia risk for ethnoracial minorities using machine learning methods.
Conclusion
Machine learning studies have made limited progress in understanding dementia risk among ethnoracial minorities.
Supporting Evidence
- The review included 14 studies published since 2020.
- Most studies found a lower risk of dementia among non-Hispanic Whites compared to other groups.
- Various machine learning techniques were employed, including neural networks and random forests.
- The studies often lacked adequate representation of ethnoracial minorities.
Takeaway
This study looked at how well machine learning helps understand dementia risk for different racial groups, and it found that there's still a lot we don't know.
Methodology
The study reviewed 599 articles, narrowed down to 14 that met inclusion criteria, and analyzed them for key themes.
Potential Biases
The studies primarily treated race as a predictor without adequately representing minority groups.
Limitations
Most studies lacked minority samples, which may contribute to the existing racial gap in dementia research.
Participant Demographics
The studies included non-Hispanic Whites, Hispanics, and Non-Hispanic Blacks.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website