Enhancing Diabetic Patient Care: Neural Network-Based Models for Readmission Prediction

To avoid complications and readmissions to the hospital, diabetes is a chronic condition that needs to be continuously monitored and managed. In order to lower readmission rates, proactive interventions are made possible by machine learning (ML)-based predictive models, which have become effective tools for early identification of high-risk patients. In this study, deep learning techniques including recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are used to investigate neural network-based models for diabetic patient readmission analysis. These models use past patient data to examine important characteristics such as blood sugar levels, medication compliance, comorbidities, and previous hospital stays in order to find trends linked to readmission chances. Methods for feature selection and data preprocessing, such as normalization and principal component analysis (PCA), improve model accuracy. Furthermore, optimization techniques like adaptive learning rates and dropout layers, as well as hyperparameter tuning, enhance performance even more. In readmission prediction, the suggested approach achieves greater precision and recall than conventional machine learning techniques like logistic regression and decision trees. By ensuring interpretability through the use of explainable AI tools, healthcare professionals may make data-driven judgments. The importance of ML-based methods in the management of diabetic patients is demonstrated by this study, opening the door to more individualized treatment regimens and better medical results.