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Smart Village Health System IoT to Envisage Chronical Disease Using Artificial Neural Network

Received: 12 January 2021    Accepted: 21 December 2021    Published: 31 December 2021
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Abstract

A Smart Village Health System (SVHS) understands the infrastructure, facilities, and schemes open to its villager. The Internet of Things (IoT) transforms a village health system into an SVHS using ANN that includes schools, highways, environment, and globalization. It has been designed with the intension to provide basic healthcare facilities to the inhabitant. It also gives information about the chronic diseases by employing Artificial Neural Network (ANN). A SVHS model based on HCV dataset is proposed in this paper. The system is built with cloud and IoT to enable data by means of the patient's input parameters to be collected, indexed and visualized in a smart village. The Levenberg-Marquardt (LM), Bayesian Regularization (BR) and the Scale Conjugate Gradient (SCG) algorithms are implemented with ANN-based approach named as " IoT enabled Smart Village Health System to Predict Chronical Disease empowered with Artificial Neural Network (ToVHS)" in order to develop an efficient and smart CHP model. The evaluation of the proposed method indicates that the SCG algorithm achieves promising results with respect to accuracy and miss rates. The predicted accuracy of the proposed model shows 90.39% performance of CHP on the given factors.

Published in Internet of Things and Cloud Computing (Volume 9, Issue 4)
DOI 10.11648/j.iotcc.20210904.11
Page(s) 27-32
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

SVHS (Smart Village Health System), IoT (Internet of Things), CHP (Chronical Health Prediction)

References
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Cite This Article
  • APA Style

    Muhammad Yousif, Akifa Abbas, Zahid Hasan, Danish Ali, Muhammad Sarfraz. (2021). Smart Village Health System IoT to Envisage Chronical Disease Using Artificial Neural Network. Internet of Things and Cloud Computing, 9(4), 27-32. https://doi.org/10.11648/j.iotcc.20210904.11

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    ACS Style

    Muhammad Yousif; Akifa Abbas; Zahid Hasan; Danish Ali; Muhammad Sarfraz. Smart Village Health System IoT to Envisage Chronical Disease Using Artificial Neural Network. Internet Things Cloud Comput. 2021, 9(4), 27-32. doi: 10.11648/j.iotcc.20210904.11

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    AMA Style

    Muhammad Yousif, Akifa Abbas, Zahid Hasan, Danish Ali, Muhammad Sarfraz. Smart Village Health System IoT to Envisage Chronical Disease Using Artificial Neural Network. Internet Things Cloud Comput. 2021;9(4):27-32. doi: 10.11648/j.iotcc.20210904.11

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  • @article{10.11648/j.iotcc.20210904.11,
      author = {Muhammad Yousif and Akifa Abbas and Zahid Hasan and Danish Ali and Muhammad Sarfraz},
      title = {Smart Village Health System IoT to Envisage Chronical Disease Using Artificial Neural Network},
      journal = {Internet of Things and Cloud Computing},
      volume = {9},
      number = {4},
      pages = {27-32},
      doi = {10.11648/j.iotcc.20210904.11},
      url = {https://doi.org/10.11648/j.iotcc.20210904.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20210904.11},
      abstract = {A Smart Village Health System (SVHS) understands the infrastructure, facilities, and schemes open to its villager. The Internet of Things (IoT) transforms a village health system into an SVHS using ANN that includes schools, highways, environment, and globalization. It has been designed with the intension to provide basic healthcare facilities to the inhabitant. It also gives information about the chronic diseases by employing Artificial Neural Network (ANN). A SVHS model based on HCV dataset is proposed in this paper. The system is built with cloud and IoT to enable data by means of the patient's input parameters to be collected, indexed and visualized in a smart village. The Levenberg-Marquardt (LM), Bayesian Regularization (BR) and the Scale Conjugate Gradient (SCG) algorithms are implemented with ANN-based approach named as " IoT enabled Smart Village Health System to Predict Chronical Disease empowered with Artificial Neural Network (ToVHS)" in order to develop an efficient and smart CHP model. The evaluation of the proposed method indicates that the SCG algorithm achieves promising results with respect to accuracy and miss rates. The predicted accuracy of the proposed model shows 90.39% performance of CHP on the given factors.},
     year = {2021}
    }
    

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    AU  - Muhammad Yousif
    AU  - Akifa Abbas
    AU  - Zahid Hasan
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    Y1  - 2021/12/31
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    N1  - https://doi.org/10.11648/j.iotcc.20210904.11
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    T2  - Internet of Things and Cloud Computing
    JF  - Internet of Things and Cloud Computing
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    AB  - A Smart Village Health System (SVHS) understands the infrastructure, facilities, and schemes open to its villager. The Internet of Things (IoT) transforms a village health system into an SVHS using ANN that includes schools, highways, environment, and globalization. It has been designed with the intension to provide basic healthcare facilities to the inhabitant. It also gives information about the chronic diseases by employing Artificial Neural Network (ANN). A SVHS model based on HCV dataset is proposed in this paper. The system is built with cloud and IoT to enable data by means of the patient's input parameters to be collected, indexed and visualized in a smart village. The Levenberg-Marquardt (LM), Bayesian Regularization (BR) and the Scale Conjugate Gradient (SCG) algorithms are implemented with ANN-based approach named as " IoT enabled Smart Village Health System to Predict Chronical Disease empowered with Artificial Neural Network (ToVHS)" in order to develop an efficient and smart CHP model. The evaluation of the proposed method indicates that the SCG algorithm achieves promising results with respect to accuracy and miss rates. The predicted accuracy of the proposed model shows 90.39% performance of CHP on the given factors.
    VL  - 9
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Author Information
  • School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan

  • Department of Computer Science, Lahore Garrison University, Lahore, Pakistan

  • School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan

  • School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan

  • School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan

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