Informatics and measurement in healthcare: deep learning for diabetic patient readmission prediction

Approximately 460 million individuals were living with diabetes globally in 2023. This study explores and contrasts methods for forecasting hospital readmissions among diabetic patients by integrating traditional approaches with modern deep learning frameworks. In this work, a variety of deep learning architectures – including recurrent models like LSTM and GRU, as well as CNNs and Autoencoders – are examined along with conventional machine learning approaches. Four essential metrics – accuracy, precision, recall, and F1-score – were employed to measure and compare the effectiveness of different models. The results revealed that deep neural network methods significantly outperformed classical machine learning algorithms. Among traditional methods, the Decision Tree achieved the highest effectiveness. However, the LSTM network demonstrated superior performance, achieving scores of 0.74 for accuracy, 0.73 for precision, 0.74 for recall, and 0.73 for the F1-score. Additionally, the GRU and Vanilla LSTM models exhibited performance close to the best model, indicating that recurrent networks are more suitable for this problem than traditional methods.