Prognostic Analysis of Hyponatremia for Diseased Patients Using Multilayer Perceptron Classification Technique

Prasannavenkatesan Theerthagiri; Gopala C; Nishan H

Research Article

Prognostic Analysis of Hyponatremia for Diseased Patients Using Multilayer Perceptron Classification Technique

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@ARTICLE{10.4108/eai.17-3-2021.169032,
    author={Prasannavenkatesan Theerthagiri and Gopala Krishnan C and Nishan A H},
    title={Prognostic Analysis of Hyponatremia for Diseased Patients Using Multilayer Perceptron Classification Technique},
    journal={EAI Endorsed Transactions on Pervasive Health and Technology},
    volume={7},
    number={26},
    publisher={EAI},
    journal_a={PHAT},
    year={2021},
    month={3},
    keywords={Sodium electrolyte, Hyponatremia, MLP, Prediction, Arginine vasopressin},
    doi={10.4108/eai.17-3-2021.169032}
}

Prasannavenkatesan Theerthagiri
Gopala Krishnan C
Nishan A H
Year: 2021
Prognostic Analysis of Hyponatremia for Diseased Patients Using Multilayer Perceptron Classification Technique
PHAT
EAI
DOI: 10.4108/eai.17-3-2021.169032

Prasannavenkatesan Theerthagiri¹^,*, Gopala Krishnan C¹, Nishan A H²

1: Department of Computer Science and Engineering, GITAM School of Technology, GITAM University, Bengaluru-561203, India
2: Department of Computer Science and Engineering, Francis Xavier Engineering College, Tamil Nadu, India

*Contact email: vprasann@gitam.edu

Abstract

INTRODUCTION: The sodium electrolyte deficiency in the human serum is known as Hyponatremia. The deficiency of sodium in the blood indulges many problems for the patients. If the sodium range in human serum not managed and treated it creates difficulties such as longer hospital stays and mortality.

OBJECTIVES: This paper focuses on forecasting the sodium ranges of patient using the machine learning algorithm supported by the past health records of the patients.

METHODS: The vital patient information including the disease history, age, gender, and serum sodium level before and after hospital admission are analysed using the logistic regression, k-nearest neighbour, multilayer perceptron, and extra-trees ensemble classification algorithm.The results of the classification algorithm show that the proposed MLP algorithm produces higher prediction results as compared to other machine learning algorithms. Also, the confusion matrix, Kappa score, R square value and error metrics.

CONCLUSION: The results show that the MLP classification is more suitable prognostic analysis of the hyponatremia for diseased patients.

Keywords: Sodium electrolyte, Hyponatremia, MLP, Prediction, Arginine vasopressin

Received: 2020-07-24
Accepted: 2021-01-27
Published: 2021-03-17
Publisher: EAI

: http://dx.doi.org/10.4108/eai.17-3-2021.169032

Copyright © 2021 Prasannavenkatesan Theerthagiri et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.