Improving Dementia Classification Using Transfer Learning
Author Information
Author(s): Leist Anja, Glymour M Maria, Langa Kenneth, Kim Jung Hyun
Primary Institution: University of Luxembourg
Hypothesis
Can transfer learning improve the accuracy of dementia classification across different racial and ethnic groups?
Conclusion
Transfer learning can enhance the accuracy of dementia classification for underrepresented groups, leading to better public health research.
Supporting Evidence
- The transfer-learned algorithm had higher accuracy than the best previously reported algorithm for non-Hispanic Black participants.
- The transfer-learned algorithm improved model calibration for Hispanic participants.
Takeaway
This study shows that using a special technique called transfer learning can help make better guesses about dementia in different groups of people.
Methodology
The study used data from two sources: a large dataset for initial algorithm development and a smaller dataset for refining the model.
Participant Demographics
Participants included non-Hispanic Black and Hispanic individuals.
Digital Object Identifier (DOI)
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