TY  - JOUR
Y1  - 2021///
VL  - 72
UR  - 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
JF  - Lecture Notes on Data Engineering and Communications Technologies
A1  - Thangarasu, G.
A1  - Dominic, P.D.D.
A1  - Subramanian, K.
KW  - Clustering algorithms; Fuzzy logic; Network architecture
KW  -  Combination of neural-network; Data clustering algorithm; Fuzzy logic techniques; Healthcare services; Information and Communication Technologies; Intelligent architectures; Physical symptoms; Prototype system
KW  -  Diagnosis
ID  - scholars15785
N2  - 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.
SN  - 23674512
PB  - Springer Science and Business Media Deutschland GmbH
EP  - 110
AV  - none
SP  - 102
TI  - An Architecture for Intelligent Diagnosing Diabetic Types and Complications Based on Symptoms
N1  - cited By 0
ER  -