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Advanced LSTM Neural Networks for Predicting Hospital Readmissions in Diabetic Patients
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Author(s): Ganesh Khekare (Vellore Institute of Technology, India), Priya Dasarwar (Symbiosis International University, India), Ajay Kumar Phulre (VIT Bhopal University, India), Urvashi Khekare (Vellore Institute of Technology, India), Gaurav Kumar Ameta (Parul University, India)and Shashi Kant Gupta (Eudoxia Research University, USA)
Copyright: 2025
Pages: 22
Source title:
Expert Artificial Neural Network Applications for Science and Engineering
Source Author(s)/Editor(s): Lingala Syam Sundar (Prince Mohamamd Bin Fahd University, Saudia Arabia), Deepanraj Balakrishnan (Prince Mohammad Bin Fahd University, Saudi Arabia)and Antonio C.M. Sousa (University of Aveiro, Portugal)
DOI: 10.4018/979-8-3693-7250-0.ch006
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Abstract
The development of the predictive model for forecasting hospital readmissions among diabetic patients represents a remarkable footstep in applying machine learning techniques. These are responsible for enhancing healthcare delivery. Ethical considerations, such as transparent judgment and bias monitoring, must be precisely addressed to uphold fairness and convince the therapist. The proposed model utilizes Long Short-Term Memory (LSTM) neural networks. The model has indicated a magnificent accuracy rate of 83% during pilot testing, this development results in a 39% reduction in the 30-day readmission rate with cost effective and enhanced diagnosis solutions. Ongoing research efforts should enhance model interpretability, explore new data sources, and maintain relevance through evolving architectures and methodologies. By addressing these multifaceted challenges through a comprehensive and iterative approach, this predictive model can potentially revolutionize chronic illness management, leading to improved patient outcomes and reduced operational costs within healthcare systems.
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