Artificial neural networks as clinical decision support systems. (4th May 2021)

• Record Type: Journal Article
• Authors: Shafi, Imran; Ansari, Sana; Din, Sadia; Jeon, Gwanggil; Paul, Anand
• Other Names: Jeon Gwanggil guestEditor.; Chehri Abdellah guestEditor.; Lu Huimin guestEditor.; Guna Jože guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 22(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-04
• Subjects: artificial neural networks -- back‐propagation -- clinical decision system -- cloud computing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6342
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In the last decade, artificial intelligent systems based on neural networks have gradually become primary source for clinical decision support systems (CDSS) and are being used in diverse areas of medical diagnosis, classification, and prediction. An artificial neural network (ANN) consists of a large number of processing units which performs the computation in a parallel and distributed environment. They learn the pattern from the examples provided to it and then generalize based on the concepts they have learned while training. This paper presents a review of the current status of ANN and its variants as CDSS in various medical disciplines. The work focuses and describes the methods making use of simple ANN and use of real‐time approaches based on big data using ANN in cloud computing environment for various medical applications. Critical analysis of various methods based on smart approaches indicates that feed‐forward back propagation ANN performs sufficiently better in the domain of medicines with a high degree of accuracy.