Cluster analysis reveals socioeconomic disparities among elective spine surgery patients

Authors

• Tayo Obafemi-Ajayi, Missouri State UniversityFollow
• Alena Orlenko, Cedars-Sinai Medical Center
• Philip J. Freda, Cedars-Sinai Medical Center
• Attri Ghosh, Cedars-Sinai Medical Center
• Hyunjun Choi, Cedars-Sinai Medical Center
• Nicholas Matsumoto, Cedars-Sinai Medical Center
• Tiffani J. Bright, Cedars-Sinai Medical Center
• Corey T. Walker, Cedars-Sinai Medical Center
• Jason H. Moore, Cedars-Sinai Medical Center

Abstract

This work demonstrates the use of cluster analysis in detecting fair and unbiased novel discoveries. Given a sample population of elective spinal fusion patients, we identify two overarching subgroups driven by insurance type. The Medicare group, associated with lower socioeconomic status, exhibited an over-representation of negative risk factors. The findings provide a compelling depiction of the interwoven socioeconomic and racial disparities present within the healthcare system, highlighting their consequential effects on health inequalities. The results are intended to guide design of fair and precise machine learning models based on intentional integration of population stratification.

Department(s)

Cooperative Engineering Program

Document Type

Conference Proceeding

DOI

10.1142/9789811286421_0028

Keywords

clustering, equity, explainability, fairness, feature importance, informatics

Publication Date

1-1-2024

Recommended Citation

Obafemi-Ajayi, Tayo; Orlenko, Alena; Freda, Philip J.; Ghosh, Attri; Choi, Hyunjun; Matsumoto, Nicholas; Bright, Tiffani J.; Walker, Corey T.; and Moore, Jason H., "Cluster analysis reveals socioeconomic disparities among elective spine surgery patients" (2024). Faculty Scholarship. 468.
https://bearworks.missouristate.edu/articles00/468

Journal Title

Pacific Symposium on Biocomputing