Title: Design and development of healthcare data classification with Gannet Geese migration optimisation

Authors: K. Johny Elma; Preethika Immaculate Britto; Velliangiri Sarveshwaran; Sakthivel Sankaran

Addresses: Department of Information Technology, Easwari Engineering College, India ' Department of Biomedical Engineering, College of Engineering, King Faisal University, Kingdom of Saudi Arabia ' Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, India ' Department of Biomedical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar – 626126, India

Abstract: A novel internet of things (IoT) with blockchain enabled healthcare medical data classification with deep learning-based optimisation algorithmic technique is developed. This research initially explored the IoT-based blockchain approach. Then, the input data obtained from the database undergo linear normalisation to transform it into a structured format. After that, the normalised data is given to the feature fusion phase to reduce the dimension of data features; here the feature fusion is performed based on deep neural network (DNN) and Matusita distance. The data augmentation is done by using the synthetic minority over-sampling technique (SMOTE). The augmented data are allowed for medical data classification utilising deep residual network (DRN) which is trained using Gannet Geese migration optimisation (GGMO). The GGMO-based DRN gives better efficiency with augmented accuracy of 92.2%, TPR of 90.2%, and TNR of 92.4%.

Keywords: internet of things; IoT with blockchain; medical data classification; linear normalisation; Matusita distance; deep neural network; DNN; deep residual network; DRN.

DOI: 10.1504/IJAHUC.2025.146436

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.49 No.2, pp.75 - 91

Received: 04 Mar 2024
Accepted: 18 Sep 2024
Published online: 29 May 2025 *

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