Effectiveness of a hybrid deep learning model integrated with a hybrid parameterisation model in decision-making analysis

• Published in: Frontiers in Artificial Intelligence and Applications
• Main Author: Mohamad M.; Selamat A.
• Format: Conference paper
• Language: English
• Published: IOS Press BV 2020
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092702492&doi=10.3233%2fFAIA200551&partnerID=40&md5=b4975aae85a785cb6cf6c10b4295c1ca

Deep learning has recently gained the attention of many researchers in various fields. A new and emerging machine learning technique, it is derived from a neural network algorithm capable of analysing unstructured datasets without supervision. This study compared the effectiveness of the deep learning (DL) model vs. a hybrid deep learning (HDL) model integrated with a hybrid parameterisation model in handling complex and missing medical datasets as well as their performance in increasing classification. The results showed that 1) the DL model performed better on its own, 2) DL was able to analyse complex medical datasets even with missing data values, and 3) HDL performed well as well and had faster processing times since it was integrated with a hybrid parameterisation model. © 2020 The authors and IOS Press. All rights reserved.