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IoMT-based smart healthcare monitoring system using adaptive wavelet entropy deep feature fusion and improved RNN

  • Track 2: Medical Applications of Multimedia
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Abstract

With the help of pervasive computing, human living has changed into a smarter way using the developments in IoMT, telecommunication technologies, and wearable sensors for ensuring improved healthcare services. IoMT is comprised of certain potentiality for the revolution in the healthcare industry. IoMT is associated with caregivers, healthcare providers, patients, and wearable sensors with software and ICT. The healthcare industry is also a well-known expanding market that has huge demands. It ensures the potential services towards the patients and also provides its contributions to the profits of the health sector. According to the technical advancements, a healthcare system must be developed based on decision-making capacity. Numerous researchers have also focused on involving cognitive behavior in IoT technology. Thus, in this paper, a new smart healthcare system with the help of IoT devices is suggested. Initially, the data is collected from IoMT devices, which are fed to further processing. Secondly, the data pre-processing is carried out to remove the corrupted data and for removing the noise from the data. Thirdly, the features are collected from the pre-processed data through wavelet entropy computation, and deep features are gathered using CNN. Fourthly, both extracted wavelet entropy features and deep features have undergone an adaptive fusion process using an improved meta-heuristic algorithm, thus termed adaptive wavelet entropy deep feature fusion. Finally, the classification is performed through I-RNN to get the disease-related outcomes, where the weight of RNN is optimized using a new MVS-AVOA. Through the evaluation, the performance analysis of the proposed MVS-AVOA-RNN has 41.5% better than Naive Bayes, 26.8% better than SRU, 18.3% superior to LSTM, and 5.4% enriched than RNN. Thus, the obtained result reveals that the proposed optimized RNN with an advanced feature set supersedes the aforementioned techniques.

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Data availability

The data underlying this article are available in Breast Cancer Wisconsin (Diagnostic) Data Set, at https://www.kaggle.com/uciml/breast-cancer-wisconsin-data, PIMA at https://www.kaggle.com/uciml/pima-indians-diabetes-database, Heart Disease Dataset (Comprehensive) at https://www.kaggle.com/sid321axn/heart-statlog-cleveland-hungary-final and Parkinson’s Disease Classification Data Set at https://archive.ics.uci.edu/ml/datasets/Parkinson%27s+Disease+Classification.

Abbreviations

AVOA:

African Vultures Optimization Algorithm

CAD:

Computer Aided Diagnosis

CNN:

Convolutional Neural Networks

DAI:

Distributed Artificial Intelligence

GENI:

Global Environment for Network Innovations

HTSMNN:

Heuristic Tubu Optimized Sequence Modular Neural Network

IBoNN:

Intelligent Agent-based Bag-of-Neural Networks

ICT:

Information and Communication Technology

IoMT:

Internet of Medical Things

IOT:

Internet of Things

I-RNN:

Improved Recurrent Neural Network

LSTM:

Long Short-Term Memory

MVS-AVOA:

Modified Vulture Satiation-based African Vultures Optimization Algorithm

PSO:

Particle Swarm Optimization

PART:

Partial Tree

ReLU:

Rectified Linear Units

SFO:

Sun Flower Optimization

SRU:

Simple Recurrent Unit

SOM:

Self-Organizing Map

VIRFIM:

Virus Resistance Framework using the Internet of Medical Things

WOA:

Whale Optimization Algorithm

g(u):

Wavelet function

AEn :

Approximation entropy

\( D{T}_v^{in} \) :

Data

\( {c}_{ii}^{Di}(Ri) \) :

Wavelet Probability

SE(Di, Ri, n):

Sample entropy

\( F{T}_u^{wet} \) :

Entropy features

\( F{T}_f^{cnn} \) :

Wavelet entropy function

\( F{T}_u^{opwet} \) :

Optimized weighted of wavelet function

\( F{T}_f^{opcnn} \) :

Optimized weighted deep features

fF, G :

tanh and softmax activation functions

maxitr :

Maximum iteration

ff 1 :

satiation

\( R{N}_{Q_1}^s \) :

Random parameter

\( {P}_j^{s+1} \) :

Location

RN pp1 :

Random numbers

dd(j):

Distance

\( {W}_t^{rnn} \) :

Weight

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Authors and Affiliations

  1. Department of Basic Sciences, Riyadh Elm University (REU), Riyadh, Kingdom of Saudi Arabia

    MD. Mobin Akhtar & Raid Saleh Ali Shatat

  2. College of Administrative Sciences, Applied Science University, Al Eker, Kingdom of Bahrain

    Abdallah Saleh Ali Shatat

  3. Department of Computer Science, Imam Muhammad IBN Saud Islamic University Riyadh, Riyadh, Kingdom of Saudi Arabia

    Shabi Alam Hameed

  4. Business Management, Applied Science University, Al Eker, Kingdom of Bahrain

    Sakher (M.A) Ibrahim Alnajdawi

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  1. MD. Mobin Akhtar
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Akhtar, M.M., Shatat, R.S.A., Shatat, A.S.A. et al. IoMT-based smart healthcare monitoring system using adaptive wavelet entropy deep feature fusion and improved RNN. Multimed Tools Appl 82, 17353–17390 (2023). https://doi.org/10.1007/s11042-022-13934-5

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  • DOI: https://doi.org/10.1007/s11042-022-13934-5

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