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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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Published Paper Details:
Diagnosis of diabetes may be achieved via the use of deep learning and convolutional long short-term memory networks.
Authors
Harshita Rathi,Ashok Kumar Behera
Downlaod Center
Keywords
Convolutional long short-term memory; diabetes prediction; machine learning; pre-processing
Abstract
With the rising prevalence of diabetes worldwide, there is a growing need for accurate and timely detection methods to enable early intervention and management. In this study, we propose a novel approach for diabetes detection leveraging the power of deep learning techniques, specifically employing Convolutional Long Short-Term Memory (ConvLSTM) networks.
Our methodology involves the integration of ConvLSTM networks, which combine the spatial understanding of convolutional layers with the temporal memory capabilities of LSTM layers. This unique architecture enables the model to capture both spatial and temporal dependencies within sequential medical data, such as time-series information from glucose monitoring.
To validate the efficacy of our approach, we conducted experiments on a comprehensive dataset comprising diverse patient profiles. The dataset includes information such as glucose levels, insulin sensitivity, and other relevant health indicators. Our proposed ConvLSTM-based model demonstrates superior performance in diabetes detection compared to traditional machine learning methods and other deep learning architectures.
Furthermore, we explore the interpretability of the model predictions, providing insights into the key features and temporal patterns influencing diabetes detection. This not only enhances the model's transparency but also contributes to a better understanding of the complex relationships within the data.
The results of our study indicate that the proposed ConvLSTM-based approach holds promise as an effective and accurate tool for diabetes detection. The integration of deep learning techniques in healthcare, particularly in diabetes diagnosis, can pave the way for more personalized and timely interventions, ultimately improving patient outcomes.
IJCRT's Publication Details
Unique Identification Number - IJCRT2402260
Paper ID - 251077
Author type - Indian Author
Page Number(s) - c256-c267
Pubished in - Volume 12 | Issue 2 | February 2024
DOI (Digital Object Identifier) -
No Of Downloads - 69
Author Country - India, 491001, Durg, Durg, 491001, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Harshita Rathi,Ashok Kumar Behera, "Diagnosis of diabetes may be achieved via the use of deep learning and convolutional long short-term memory networks.", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 2, pp.c256-c267, February 2024, Available at :http://www.ijcrt.org/papers/IJCRT2402260.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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