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Paediatrics Respiratory Diseases Diagnostic and Prediction System using Deep Learning Model

by Oluwajana Kehinde Joseph, Adegun Iyanu Pelumi, Oluwadare Samuel Adebayo
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 13 - Number 1
Year of Publication: 2025
Authors: Oluwajana Kehinde Joseph, Adegun Iyanu Pelumi, Oluwadare Samuel Adebayo
10.5120/ijais2025452011

Oluwajana Kehinde Joseph, Adegun Iyanu Pelumi, Oluwadare Samuel Adebayo . Paediatrics Respiratory Diseases Diagnostic and Prediction System using Deep Learning Model. International Journal of Applied Information Systems. 13, 1 ( Aug 2025), 18-27. DOI=10.5120/ijais2025452011

@article{ 10.5120/ijais2025452011,
author = { Oluwajana Kehinde Joseph, Adegun Iyanu Pelumi, Oluwadare Samuel Adebayo },
title = { Paediatrics Respiratory Diseases Diagnostic and Prediction System using Deep Learning Model },
journal = { International Journal of Applied Information Systems },
issue_date = { Aug 2025 },
volume = { 13 },
number = { 1 },
month = { Aug },
year = { 2025 },
issn = { 2249-0868 },
pages = { 18-27 },
numpages = {9},
url = { https://www.ijais.org/archives/volume13/number1/paediatrics-respiratory-diseases-diagnostic-and-prediction-system-using-deep-learning-model/ },
doi = { 10.5120/ijais2025452011 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-08-29T00:23:15.265510+05:30
%A Oluwajana Kehinde Joseph
%A Adegun Iyanu Pelumi
%A Oluwadare Samuel Adebayo
%T Paediatrics Respiratory Diseases Diagnostic and Prediction System using Deep Learning Model
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 13
%N 1
%P 18-27
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The study presents a deep learning-based diagnostic and prediction method for paediatric respiratory illnesses that have a significant global impact on children's health, notably upper respiratory tract infections (URTI), chronic obstructive pulmonary disease (COPD), bronchiolitis, and pneumonia. The proposed framework combines Gated Recurrent Units (GRU) to describe sequential patterns in Mel-Frequency Cepstral Coefficients (MFCCs) and Convolutional Neural Networks (CNN) to capture local temporal features through the analysis of respiratory sound recordings. The model, which was trained on a labelled dataset, performed 84% of the time and showed good diagnostic and prediction abilities, particularly for cases of bronchiolitis (92% precision and recall) and healthy (100% precision and recall). The model's potential as an accurate and easily accessible tool for diagnosing and predicting paediatric respiratory diseases is proven by the results, despite a few misclassifications.

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Index Terms

Computer Science
Information Sciences

Keywords

Convolutional Neural Networks (CNN) Gated Recurrent Units (GRU) COPD Healthy Bronchiolitis Pneumonia

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