An ensemble of deep representation learning with metaheuristic optimisation algorithm for critical health monitoring using internet of medical things

Abstract

The Internet of Things (IoT) plays a significant part in the healthcare field. The growth of smart devices, smart sensors, and advanced lightweight communication protocols has created an opportunity to connect medical devices for monitoring biomedical signals and identifying patients' illnesses without human involvement, known as the Internet of Medical Things (IoMT). The IoMT enables a medical method to connect various smart devices, such as hospital assets, wearable sensors, and medical examination instruments, to create an information platform. In recent times, the IoMT has been extensively utilized in various areas, including disease diagnosis, smart hospitals, infectious disease tracking, and remote health monitoring. Still, safety is one of the key requirements for the success of IoMT systems. Thus, at present, deep learning (DL) is considered a safe IoMT system, as it can enhance the system's performance. In this manuscript, the Ensemble of Deep Learning and Metaheuristic Optimisation algorithms for the Critical Health Monitoring (EDLMOA-CHM) technique is proposed. The EDLMOA-CHM technique aims to develop and evaluate effective methods for monitoring health conditions in the IoMT to enhance healthcare system security and patient safety. Initially, the Z-score normalization method is employed in the data pre-processing step to clean, transform, and organize raw data into an appropriate format. For the feature selection process, the binary grey wolf optimization (BGWO) model is employed to identify and retain the most significant features in the dataset. The classification process utilizes ensemble models, including the Temporal Convolutional Network (TCN), the Attention-based Bidirectional Gated Recurrent Unit (A-BiGRU), and the Hybrid Deep Belief Network (HDBN) techniques. To further optimize model performance, the pelican optimization algorithm (POA) is utilized for hyperparameter tuning to ensure that the optimum hyperparameters are chosen for enhanced accuracy. To demonstrate the improved performance of the EDLMOA-CHM model, a comprehensive experimental analysis is conducted using the healthcare IoT dataset. The comparison analysis of the EDLMOA-CHM model demonstrated a superior accuracy value of 99.56% over existing techniques.

Keywords: Critical health monitoring; Ensemble deep learning; Feature selection; Internet of medical things; Metaheuristic optimisation algorithm.

Fig. 1

General architecture of IoMT.

Fig. 2

Overall working flow procedure of EDLMOA-CHM model.

Fig. 3

Structure of the attention-based Bi-GRU model.

Fig. 4

Correlation matrix of EDLMOA-CHM model.

Fig. 5

Histogram and KDE outcome of EDLMOA-CHM model.

Fig. 6

Confusion matrices of EDLMOA-CHM technique (a-f), Epochs 500–3000.

Fig. 7

Average values of EDLMOA-CHM technique (a-f), Epochs 500–3000.

Fig. 8

curve of EDLMOA-CHM method under Epoch 3000.

Fig. 9

Loss curve of EDLMOA-CHM method under Epoch 3000.

Fig. 10

PR curve of EDLMOA-CHM method under Epoch 3000.

Fig. 11

ROC curve of EDLMOA-CHM method under Epoch 3000.

Fig. 12

, and outcome of EDLMOA-CHM model with existing approaches.

Fig. 13

and outcome of EDLMOA-CHM model with existing approaches.

Fig. 14

CT outcome of EDLMOA-CHM technique with existing methods.