Open Access Peer-reviewed Research Article

High-Dimensional Feature Space for Diabetes Diagnosis and Identification of Diabetic-Sensitive Features in Ayurvedic Nadi Signals

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Submitted   Aug 24, 2023
Published   Oct 13, 2023
Issue    Vol 1 No 1 (2023)
DOI   10.25082/CRTM.2023.01.001

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Jayani Umasha corresponding author
Department of Electrical and Electronic Engineering, University of Peradeniya, Peradeniya, 20400, Sri Lanka
Janaka Wijayakulasooriya
Department of Electrical and Electronic Engineering, University of Peradeniya, Peradeniya, 20400, Sri Lanka
Ruwan Ranaweera
Department of Electrical and Electronic Engineering, University of Peradeniya, Peradeniya, 20400, Sri Lanka

Abstract

Nadi-based disease diagnosis is a traditional art in Ayurvedic medicine that is an inquisitive yet not widely comprehended subject. A collection of higher dimensional features from a preprocessed Nadi dataset was extracted and analyzed to diagnose diabetes. The t-distributed Stochastic Neighbor Embedding was used to visualize the higher dimensional feature space in 2-D. The linear dimensionality reduction method of Principal Component Analysis and several linear and nonlinear classifiers were tested on the reduced feature space in identifying diabetes. The key outcomes of this paper are the ability to reduce the feature space by 73.33% while retaining a classification accuracy of 95.4%, identifying age as a compounding factor in diagnosis, and extracting the diabetes-sensitive features with eigenvalue loading.

Keywords
Nadi, higher-dimensional features, t-SNE, PCA, diabetes diagnosis, wearable sensors

Article Details

How to Cite
Umasha, J., Wijayakulasooriya, J., & Ranaweera, R. (2023). High-Dimensional Feature Space for Diabetes Diagnosis and Identification of Diabetic-Sensitive Features in Ayurvedic Nadi Signals. Current Research in Traditional Medicine, 1(1), 1-9. https://doi.org/10.25082/CRTM.2023.01.001
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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