Review of Data Bias in Healthcare Applications

Authors

Atharva Prakash Parate, Vellore Institute of Technology
Aditya Ajay Iyer, Vellore Institute of Technology
Kanav Gupta, Vellore Institute of Technology
Harsh Porwal, Vellore Institute of Technology
P. C. Kishoreraja, Vellore Institute of TechnologyFollow
R. Sivakumar, Vellore Institute of Technology
Rahul Soangra, Chapman UniversityFollow

Document Type

Article

Publication Date

9-13-2024

Abstract

In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus on different methods used for creating synthetic data. This paper explores several mitigating algorithms like SMOTE, AdaSyn, Fair-SMOTE, and BayesBoost. The optimized Bayesboost algorithm has been discussed. This approach showed more accuracy and addressed the error handling mechanism.

Comments

This article was originally published in International Journal of Online and Biomedical Engineering (iJOE), volume 20, issue 12, in 2024. https://doi.org/10.3991/ijoe.v20i12.49997

Recommended Citation

Parate, A.P., Iyer, A.A., Gupta, K., Porwal, H., Kishoreraja, P.C., Sivakumar, R., Soangra, R. (2024). Review of Data Bias in Healthcare Applications. International Journal of Online and Biomedical Engineering (iJOE), 20(12), pp. 124–136. https://doi.org/10.3991/ijoe.v20i12.49997

Peer Reviewed

1

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.