Diagnosis of community-acquired pneumonia in children using photoplethysmography and machine learning-based classifier

Kanwal, K, Khalid, SG ORCID: https://orcid.org/0000-0002-0305-5678, Asif, M, Zafar, F and Qurashi, AG, 2024. Diagnosis of community-acquired pneumonia in children using photoplethysmography and machine learning-based classifier. Biomedical Signal Processing and Control, 87 (Part A): 105367. ISSN 1746-8094

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Abstract

This paper presents a novel approach for diagnosing Community-Acquired Pneumonia (CAP) in children using single-channel photoplethysmography (PPG) using machine learning Traditional diagnostic methods (x-rays systems and blood tests) for pneumonia suffer from limitations e.g., unavailability in remote rural areas, time consumption, financial burden, and reliance on invasive procedures. This novel approach uses the PPG recording alone to generate accurate and rapid diagnoses of CAP in children that may facilitate healthcare practitioners in low-resource clinical settings in future. A cross-sectional study was carried out to collect the PPG recordings of 67 paediatric participants (31 CAP and 36 healthy). Five different machine learning classifiers namely Fine Decision tree, Linear Discriminant Analysis, Weighted K Nearest Neighbour, Wide Neural Network, and Ensemble of Bagged Trees using eight PPG signal features were employed. Using weighted KNN we predicted 9 out of 10 test subjects correctly. These results demonstrate the potential of the system to improve clinical decision-making and patient outcomes since despite the thriving advancements in healthcare paediatric pneumonia remains a major health concern.

Item Type:	Journal article
Publication Title:	Biomedical Signal Processing and Control
Creators:	Kanwal, K., Khalid, S.G., Asif, M., Zafar, F. and Qurashi, A.G.
Publisher:	Elsevier
Date:	January 2024
Volume:	87
Number:	Part A
ISSN:	1746-8094
Identifiers:	Number Type 10.1016/j.bspc.2023.105367 DOI S1746809423008005 Publisher Item Identifier 1910161 Other
Rights:	© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Melissa Cornwell
Date Added:	05 Jul 2024 15:21
Last Modified:	05 Jul 2024 15:21
URI:	https://irep.ntu.ac.uk/id/eprint/51701

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