Machine learning models can help predict which patients with diabetes are likely to be readmitted to hospital, but they need to work fairly for all patients. This study compared several of these models, including deep learning, generalised linear models, gradient boosting machines and naive Bayes, assessing not only their accuracy but also how fair their predictions were across age, gender and race.
The gradient boosting model performed best overall, with an accuracy of 82.2% and consistently low error rates for most racial and gender groups. However, across several of the models, African American and Hispanic patients had higher rates of false positives, meaning they were more likely to be wrongly predicted to be readmitted. No single model performed best for every group: some worked better for certain groups of patients and others for other groups.
The authors conclude that there is no one-size-fits-all solution, and that models need to be chosen and adjusted carefully to ensure they are both accurate and fair for all patients. According to them, fair algorithms can help reduce disparities and improve outcomes for patients with diabetes from all backgrounds.
Learn more about this study here: https://doi.org/10.1109/ICHI61247.2024.00105
Reference:
Al-Zanbouri, Z., Sharma, G., & Raza, S. (2024). Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions. In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI) (pp. 660–669). IEEE.