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A Hyb-WGWO and Deep EMC–based Intelligent E-healthcare monitoring model for patient condition diagnosis in internet of things connected applications

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Abstract

Purpose

This manuscript proposes an IoT-based E-healthcare monitoring method for constantly monitoring the important signs of patient and diagnoses biological and behavioral changes through the technologies of smart healthcare.

Method

Patient health monitoring system is used to deep learning techniques for the performance of patient categorization. Here, Hybrid whale and grey wolf optimization approach (Hyb-WGWO) is proposed to the selection of cluster head with various internet of things (IoTs) devices. These devices are utilized to sense the healthcare data into clusters form. In this, the cluster head is chosen by using the proposed Hyb-WGWO approach. Data in the cloud server are transmitted to the cluster head. Then deep ensemble multitask classification (DEMC) model is proposed for identifying patient disease conditions, such as low, normal, abnormal and critical.

Results

The experimental outcomes of the proposed Hyb-WGWO-DEMC approach show 25.98% and 31.86% improved accuracy, 28.08%, 26.87% better specificity than the existing methods, such as Elliptic Decryption Algorithm in Elman Neural Network (EDA-ENN) and Grasshopper Optimization and Particle Swarm Optimization with cryptographic encryption (GOPSO-CE) respectively.

Conclusion

IoT-based E-healthcare monitoring method achieves higher accuracy and lower error rate through the technologies of smart healthcare.

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Data sharing does not apply to this article as no new data has been created or examined in this study.

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Authors and Affiliations

  1. RNTBCI Chennai, Tamil Nadu, Chengalpattu, 603 002, India

    Ramesh Kumar Mojjada

  2. Department of Electronics and Communication Engineering, Matrusri Engineering College, Saidabad, Hyderabad, Telangana, 500 059, India

    K. Ashok Kumar

  3. Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Nagar, Thandalam, Chennai, Tamil Nadu, 602 105, India

    C. Tamizhselvan

  4. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology,, Ramapuram campus, Chennai, Tamil Nadu, 500 089, India

    R. Surender

  5. Department of Computer Science and Engineering,, Dhanekula Institute of Engineering and Technology, Krishna District, Vijayawada, Andhra Pradesh, 521 139, India

    Sunil Babu Melingi

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  1. Ramesh Kumar Mojjada
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  2. K. Ashok Kumar
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  3. C. Tamizhselvan
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  4. Sunil Babu Melingi
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Correspondence to Ramesh Kumar Mojjada.

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Mojjada, R.K., Ashok Kumar, K., Tamizhselvan, C. et al. A Hyb-WGWO and Deep EMC–based Intelligent E-healthcare monitoring model for patient condition diagnosis in internet of things connected applications. Res. Biomed. Eng. 39, 37–49 (2023). https://doi.org/10.1007/s42600-022-00248-6

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  • DOI: https://doi.org/10.1007/s42600-022-00248-6

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