Please use this identifier to cite or link to this item:
http://repository.kln.ac.lk/handle/123456789/28105
Title: | Non-invasive diagnostic approach for diabetes using pulse wave analysis and deep learning |
Authors: | Gunathilaka, H. Rajapaksha, R. Kumarika, T. Perera, D. Herath, U. Jayathilaka, C. Liyanage, J. Kalingamudali, S. |
Keywords: | Convolutional neural network (CNN) Non-invasive diabetes diagnosis Pulse wave analysis (PWA) |
Issue Date: | 2024 |
Publisher: | MDPI |
Citation: | Informatics. 2024; 11(3): 51. |
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. |
Description: | Not Indexed |
URI: | http://repository.kln.ac.lk/handle/123456789/28105 |
ISSN: | 2227-9709 |
Appears in Collections: | Journal/Magazine Articles |
Files in This Item:
File | Description | Size | Format | |
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Non_Invasive Diagnostic Approach for Diabetes Using Pulse.pdf | 3.25 MB | Adobe PDF | View/Open |
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