The role of deep learning in predicting disease progression in diabetic patients

Adefemi Ayodele 1, *, Oyeniyi Babalola 2 and Temitope Adeyemo 3

1 Department of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London, United Kingdom
2 Department of Computer Science and Engineering, Faculty of Engineering and Technology, Ladoke Akintola University of Technology, Nigeria.
3 Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester metropolitan University, United Kingdom.
 
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2023, 10(02), 416-425.
Article DOI: 10.30574/wjaets.2023.10.2.0300
Publication history: 
Received on 30 October 2023; revised on 19 December 2023; accepted on 22 December 2023
 
Abstract: 
The worldwide healthcare system confronts a substantial challenge from diabetes mellitus, which requires creative methods to handle the disease. Prolonged monitoring with deep learning techniques enables healthcare professionals to detect complicated medical patterns which aid in preliminary patient diagnosis and response. This research adopts Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Hybrid Neural Network integrated with Genetic Algorithm (Hybrid NN + GA) as deep learning structural networks for diabetic disease prognostication. The Diabetes 130-US Hospitals dataset served as the foundation for trainer and test protocols for the model while containing extensive clinical electronic health records about diverse patient information. AUC value assessment together with RMSE tested the models through accuracy evaluations. There is evidence that the hybrid approach excels at generalization in medical settings by surpassing both LSTM and GRU models when analyzing imbalanced hospital records. The research proves that joint utilization of deep learning frameworks produces better clinical decision systems for precise diabetes monitoring that enhances operational efficiency. 
 
Keywords: 
Deep learning; Hybrid Neural Networks; Diabetes prediction; LSTM (Long Short-Term Memory); Gated recurrent unit (GRU); Telemedicine
 
Full text article in PDF: 

Click here

 
Copyright information: 
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0