Equity in Healthcare: Analyzing Disparities in Machine Learning
  Predictions of Diabetic Patient Readmissions
Zainab Al-Zanbouri Gauri Sharma Shaina Raza
This study investigates how machine learning (ML) models can predict hospital
readmissions for diabetic patients fairly and accurately across different
demographics (age, gender, race). We compared models like Deep Learning,
Generalized Linear Models, Gradient Boosting Machines (GBM), and Naive Bayes.
GBM stood out with an F1-score of 84.3% and accuracy of 82.2%, accurately
predicting readmissions across demographics. A fairness analysis was conducted
across all the models. GBM minimized disparities in predictions, achieving
balanced results across genders and races. It showed low False Discovery Rates
(FDR) (6-7%) and False Positive Rates (FPR) (5%) for both genders.
Additionally, FDRs remained low for racial groups, such as African Americans
(8%) and Asians (7%). Similarly, FPRs were consistent across age groups (4%)
for both patients under 40 and those above 40, indicating its precision and
ability to reduce bias. These findings emphasize the importance of choosing ML
models carefully to ensure both accuracy and fairness for all patients. By
showcasing effectiveness of various models with fairness metrics, this study
promotes personalized medicine and the need for fair ML algorithms in
healthcare. This can ultimately reduce disparities and improve outcomes for
diabetic patients of all backgrounds.