Current Research in Traditional Medicine <p><a title="Reviewer Credits" href="" target="_blank" rel="noopener"><img class="journalreviewercredits" src="/journal/public/site/images/jasongong/Logo_ReviewerCredits-journal.jpg" alt="" align="right"></a><strong>Current Research in Traditional Medicine </strong>(ISSN:2591-7757) is an open access, peer reviewed journal, publishing original research, review articles, discussion and perspectives encompasses research of traditional medicine being investigated in lab, pre-clinical/clinical application, or even criticism. The aim of the journal is to provide the authors a timely and peer reviewed process for evaluation and publication of their manuscripts.&nbsp;<br>Topics of interest include, but are not limited to the following:<br> • Natural medicine<br> • Herbal medicine<br> • Developmental-Behavioral Medicine<br> • Folk medicine<br> • Pharmacology and Toxicology<br> • Pharmacokinetics <br> • Acupuncture and moxibustion<br> • Masseotherapy <br> • Palliative care<br> • Dietary therapy<br> • Integrative complementary medicine<br> • Safety concerns and other criticism</p> en-US <p>Authors contributing to&nbsp;<em>Current Research in Traditional Medicine</em>&nbsp;agree to publish their articles under the&nbsp;<a href="">Creative Commons Attribution-Noncommercial 4.0 International License</a>, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit, that the work is not used for commercial purposes, and that in the event of reuse or distribution, the terms of this license are made clear.</p> (Snowy Wang) (Alan Tan) Fri, 13 Oct 2023 17:43:21 +0800 OJS 60 High-Dimensional Feature Space for Diabetes Diagnosis and Identification of Diabetic-Sensitive Features in Ayurvedic Nadi Signals <p>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.</p> Jayani Umasha, Janaka Wijayakulasooriya, Ruwan Ranaweera ##submission.copyrightStatement## Fri, 13 Oct 2023 17:43:05 +0800