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Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers

Background - Recently, there have been attempts to develop mHealth applications for asthma self-management. However, there is a lack of applications that can offer accurate predictions of asthma exacerbation using the weather triggers and demographic characteristics to give tailored response to user...

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Main Authors: Ho, Sin-Ban, Haque, Radiah, Chai, Ian, Abdullah, Adina
Format: Article
Published: F1000 Research Ltd 2021
Subjects:
R Medicine (General)
Online Access:http://eprints.um.edu.my/35791/
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spelling my.um.eprints.357912023-11-27T07:49:57Z http://eprints.um.edu.my/35791/ Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers Ho, Sin-Ban Haque, Radiah Chai, Ian Abdullah, Adina R Medicine (General) Background - Recently, there have been attempts to develop mHealth applications for asthma self-management. However, there is a lack of applications that can offer accurate predictions of asthma exacerbation using the weather triggers and demographic characteristics to give tailored response to users. This paper proposes an optimised Deep Neural Network Regression (DNNR) model to predict asthma exacerbation based on personalised weather triggers. Methods - With the aim of integrating weather, demography, and asthma tracking, an mHealth application was developed where users conduct the Asthma Control Test (ACT) to identify the chances of their asthma exacerbation. The asthma dataset consists of panel data from 10 users that includes 1010 ACT scores as the target output. Moreover, the dataset contains 10 input features which include five weather features (temperature, humidity, air-pressure, UV-index, wind-speed) and five demography features (age, gender, outdoor-job, outdoor-activities, location). Results - Using the DNNR model on the asthma dataset, a score of 0.83 was achieved with Mean Absolute Error (MAE)=1.44 and Mean Squared Error (MSE)=3.62. It was recognised that, for effective asthma self-management, the prediction errors must be in the acceptable loss range (error<0.5). Therefore, an optimisation process was proposed to reduce the error rates and increase the accuracy by applying standardisation and fragmented-grid-search. Consequently, the optimised-DNNR model (with 2 hidden-layers and 50 hidden-nodes) using the Adam optimiser achieved a 94 accuracy with MAE=0.20 and MSE=0.09. Conclusions - This study is the first of its kind that recognises the potentials of DNNR to identify the correlation patterns among asthma, weather, and demographic variables. The optimised-DNNR model provides predictions with a significantly higher accuracy rate than the existing predictive models and using less computing time. Thus, the optimisation process is useful to build an enhanced model that can be integrated into the asthma self-management for mHealth application. © 2021 Haque R et al. F1000 Research Ltd 2021 Article PeerReviewed Ho, Sin-Ban and Haque, Radiah and Chai, Ian and Abdullah, Adina (2021) Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers. F1000Research, 10. ISSN 20461402, DOI https://doi.org/10.12688/f1000research.73026.1 <https://doi.org/10.12688/f1000research.73026.1>. 10.12688/f1000research.73026.1
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic R Medicine (General)
spellingShingle R Medicine (General)
Ho, Sin-Ban
Haque, Radiah
Chai, Ian
Abdullah, Adina
Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers
description Background - Recently, there have been attempts to develop mHealth applications for asthma self-management. However, there is a lack of applications that can offer accurate predictions of asthma exacerbation using the weather triggers and demographic characteristics to give tailored response to users. This paper proposes an optimised Deep Neural Network Regression (DNNR) model to predict asthma exacerbation based on personalised weather triggers. Methods - With the aim of integrating weather, demography, and asthma tracking, an mHealth application was developed where users conduct the Asthma Control Test (ACT) to identify the chances of their asthma exacerbation. The asthma dataset consists of panel data from 10 users that includes 1010 ACT scores as the target output. Moreover, the dataset contains 10 input features which include five weather features (temperature, humidity, air-pressure, UV-index, wind-speed) and five demography features (age, gender, outdoor-job, outdoor-activities, location). Results - Using the DNNR model on the asthma dataset, a score of 0.83 was achieved with Mean Absolute Error (MAE)=1.44 and Mean Squared Error (MSE)=3.62. It was recognised that, for effective asthma self-management, the prediction errors must be in the acceptable loss range (error<0.5). Therefore, an optimisation process was proposed to reduce the error rates and increase the accuracy by applying standardisation and fragmented-grid-search. Consequently, the optimised-DNNR model (with 2 hidden-layers and 50 hidden-nodes) using the Adam optimiser achieved a 94 accuracy with MAE=0.20 and MSE=0.09. Conclusions - This study is the first of its kind that recognises the potentials of DNNR to identify the correlation patterns among asthma, weather, and demographic variables. The optimised-DNNR model provides predictions with a significantly higher accuracy rate than the existing predictive models and using less computing time. Thus, the optimisation process is useful to build an enhanced model that can be integrated into the asthma self-management for mHealth application. © 2021 Haque R et al.
format Article
author Ho, Sin-Ban
Haque, Radiah
Chai, Ian
Abdullah, Adina
author_facet Ho, Sin-Ban
Haque, Radiah
Chai, Ian
Abdullah, Adina
author_sort Ho, Sin-Ban
title Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers
title_short Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers
title_full Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers
title_fullStr Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers
title_full_unstemmed Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers
title_sort optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers
publisher F1000 Research Ltd
publishDate 2021
url http://eprints.um.edu.my/35791/
_version_ 1783876636078243840
score 13.211869

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