eprintid: 15785
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/57/85
datestamp: 2023-11-10 03:30:25
lastmod: 2023-11-10 03:30:25
status_changed: 2023-11-10 02:00:24
type: article
metadata_visibility: show
creators_name: Thangarasu, G.
creators_name: Dominic, P.D.D.
creators_name: Subramanian, K.
title: An Architecture for Intelligent Diagnosing Diabetic Types and Complications Based on Symptoms
ispublished: pub
keywords: Clustering algorithms; Fuzzy logic; Network architecture, Combination of neural-network; Data clustering algorithm; Fuzzy logic techniques; Healthcare services; Information and Communication Technologies; Intelligent architectures; Physical symptoms; Prototype system, Diagnosis
note: cited By 0
abstract: Information and communication technology can play a vital role in improving healthcare services by providing new and efficient ways of diagnosing diseases. Diabetic is recognized as the fastest-growing disease in the world. Due to insufficient diagnostic mechanisms, the number of undiagnosed diabetes has been increasing day by day. And it leads to creating long term complications such as neuropathy, nephropathy, foot gangrene and so on. The objective of this study is to design an intelligent architecture for diagnosing diabetes effectively based on the individual physical symptoms. The architecture has been designed by utilizing the combination of neural networks, data clustering algorithms and fuzzy logic techniques. Subsequently, a prototype system has been developed to validate against the diagnostic architecture on the aspect of efficiency and accuracy of diagnosing diabetes, and its types and complications. The overall qualitative findings from this study scored very high, which is 94.50 accurate. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
date: 2021
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105437140&doi=10.1007%2f978-3-030-70713-2_11&partnerID=40&md5=c6bdc17a1c36f6309c7c38b2513a8b28
id_number: 10.1007/978-3-030-70713-2₁₁
full_text_status: none
publication: Lecture Notes on Data Engineering and Communications Technologies
volume: 72
pagerange: 102-110
refereed: TRUE
issn: 23674512
citation:   Thangarasu, G. and Dominic, P.D.D. and Subramanian, K.  (2021) An Architecture for Intelligent Diagnosing Diabetic Types and Complications Based on Symptoms.  Lecture Notes on Data Engineering and Communications Technologies, 72.  pp. 102-110.  ISSN 23674512