Machine Learning Versus Self-Supervised Transfer Learning for 30-Day Readmission Prediction in Diabetic Patients
Abstract
Abstract Background: Hospital readmission within 30 days of discharge is a closely watched indicator of care quality and a substantial driver of avoidable healthcare cost, and diabetes is among the chronic conditions most strongly associated with early readmission. This study compares six predictive models for 30-day readmission in patients with diabetes, aiming to establish realistic performance expectations for models built on administrative and coded clinical features alone. Methods: We used the University of California, Irvine (UCI) Diabetes 130-US Hospitals dataset (101,766 encounters, 130 hospitals, 1999-2008). Four classical classifiers (logistic regression, random forest, eXtreme Gradient Boosting [XGBoost], and Light Gradient Boosting Machine [LightGBM]) were compared against two multilayer perceptrons (MLPs), one trained from scratch and one initialized via self-supervised, denoising-autoencoder pretraining. Since tabular data lacks direct analogs of image-style pretraining, transfer learning, and augmentation, each concept was mapped to a tabular equivalent: denoising-autoencoder pretraining for transfer learning, gradient-boosted tree ensembles as the "pretrained" family for structured data, and the Synthetic Minority Oversampling Technique, applied only within cross-validation folds, for augmentation. After excluding encounters ending in death or hospice discharge, 99,340 encounters were split into stratified 70/15/15 train, validation, and test partitions…
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Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design.
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