Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

arXiv:2403.19057v1 Announce Type: new
Abstract: 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 …

abstract age arxiv bayes boosting cs.lg deep learning demographics equity gender generalized gradient healthcare hospital linear machine machine learning machines patient patients predictions race study type