Algorithm Helps Address Bias in Machine Learning
Are you developing machine learning and looking for ways to make your model generalizable to a diverse population? What if you could make your model applicable to more people using the data you already have?
In a new study, researchers from NCI CBIIT, Wake Forest School of Medicine, and the University of North Carolina, used an algorithm—the Gerchberg-Saxton (GS)
[callout]Read the full report, “Improving Equity in Deep Learning Medical Applications with the Gerchberg-Saxton Algorithm,” in the Journal of Healthcare Informatics Research.[/callout]
The GS
According to Dr. Umit Topaloglu, chief of CBIIT’s Clinical and Translational Research Informatics Branch, it was the
- 9,814 patients categorized as European American,
- 1,690 patients categorized as African American,
- 346 patients categorized as Eastern Asian American,
- 641 patients categorized as Hispanic American, and
- 1,489 patients who did not report.
When they tested their new data set using an ML model with a known bias, they saw an immediate boost in prediction accuracy.
Dr. Topaloglu said, “The GS
He added, “Enhancing model performance for underrepresented populations is vital, and this approach offers one method for improving equity—both in our ML models and in the way we care for all people with cancer.”