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Non-invasive diagnostic approach for diabetes using pulse wave analysis and deep learning

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dc.contributor.author Gunathilaka, H.
dc.contributor.author Rajapaksha, R.
dc.contributor.author Kumarika, T.
dc.contributor.author Perera, D.
dc.contributor.author Herath, U.
dc.contributor.author Jayathilaka, C.
dc.contributor.author Liyanage, J.
dc.contributor.author Kalingamudali, S.
dc.date.accessioned 2024-09-04T07:42:17Z
dc.date.available 2024-09-04T07:42:17Z
dc.date.issued 2024
dc.identifier.citation Informatics. 2024; 11(3): 51. en_US
dc.identifier.issn 2227-9709
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/28105
dc.description Not Indexed en_US
dc.description.abstract The surging prevalence of diabetes globally necessitates advancements in non-invasive diagnostics, particularly for the early detection of cardiovascular anomalies associated with the condition. This study explores the efficacy of Pulse Wave Analysis (PWA) for distinguishing diabetic from non-diabetic individuals through morphological examination of pressure pulse waveforms. The research unfolds in four phases: data accrual, preprocessing, Convolutional Neural Network (CNN) model construction, and performance evaluation. Data were procured using a multipara patient monitor, resulting in 2000 pulse waves equally divided between healthy individuals and those with diabetes. These were used to train, validate, and test three distinct CNN architectures: the conventional CNN, Visual Geometry Group (VGG16), and Residual Networks (ResNet18). The accuracy, precision, recall, and F1 score gauged each model’s proficiency. The CNN demonstrated a training accuracy of 82.09% and a testing accuracy of 80.6%. The VGG16, with its deeper structure, surpassed the baseline with training and testing accuracies of 90.2% and 86.57%, respectively. ResNet18 excelled, achieving a training accuracy of 92.50% and a testing accuracy of 92.00%, indicating its robustness in pattern recognition within pulse wave data. Deploying deep learning for diabetes screening marks progress, suggesting clinical use and future studies on bigger datasets for refinement. en_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.subject Convolutional neural network (CNN) en_US
dc.subject Non-invasive diabetes diagnosis en_US
dc.subject Pulse wave analysis (PWA) en_US
dc.title Non-invasive diagnostic approach for diabetes using pulse wave analysis and deep learning en_US
dc.type Article en_US


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