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Internet-of-things based machine learning enabled medical decision support system for prediction of health issues

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Abstract

Purpose

The main purpose of this paper is to develop and test internet of things (IoT) based physiological parameters monitoring system. This system is implemented using different multilabel classifier (MLC) algorithms and have been used for the health status prediction and classifications.

Method

A method has been proposed and developed as medical decision support system (MDSS) based on the outcome of the different MLC algorithms. The developed MDSS enables real time assistance to local and remote supervisor for in time decision better diagnosis plan

Results

The performance parameters and results of the different MLC algorithms has been evaluated, in terms of several, Accuracy, Precision, Recall F-measure, and MCC etc. to identify the best algorithm for classification and prediction of health status. Gradient Boost algorithm of classification outperform the other algorithm and achieved approx 94% of accuracy.

Conclusions

This study concludes that the developed MDSS will be very useful in remote areas to assist patient and health practitioners to predict heath status and for quick decision making in case of medical emergencies.

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Availability of data and material

On request data will be available.

Code availability

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Authors and Affiliations

  1. Department of Computer Application, National Institute of Technology Raipur (C.G), Raipur, India

    Manju Lata Sahu & Mithilesh Atulkar

  2. Department of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal (M.P), Bhopal, India

    Mitul Kumar Ahirwal

  3. Flipkart India Pvt Ltd, Bangalore, Karnatak, India

    Afsar Ahamad

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  1. Manju Lata Sahu
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  4. Afsar Ahamad
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Contributions

Mitul Kumar Ahirwal, Mithliesh Atulkar and Manju Lata Sahu contributed to the study conception and design. Material preparation, data collection and analysis were performed by Manju Lata Sahu, and Afsar Ahamad. The first draft of the manuscript was written by Manju Lata Sahu and all authors commented on previous versions of the manuscript.

Corresponding author

Correspondence to Mitul Kumar Ahirwal.

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Authors confirm that there are no known conflicts of interest connected to this publication. All authors confirm the manuscript. There is no financial support for present work that could have influenced the outcome.

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Authors confirm that whole work covered in the present manuscript involving human participants and their physiological data recoding has been conducted with the approval of ethical committee at NIT, Raipur, India.

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Sahu, M.L., Atulkar, M., Ahirwal, M.K. et al. Internet-of-things based machine learning enabled medical decision support system for prediction of health issues. Health Technol. 13, 987–1002 (2023). https://doi.org/10.1007/s12553-023-00790-y

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