Predicting Asthma Attacks Effects of Indoor Pm Concentrations on Peak Expiratory Flow Rates of Asthmatic Children

Seo, Sung Chul; Kho, Hong-Seop; Kim, K.Y.

doi:10.1016/j.chest.2020.05.009

Cited by 1 publication

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“…Asthma is a chronic lung disease that affects people of all age groups around the world. 1 Asthma exacerbation causes asthma attacks, and the frequency of asthma attacks depends on the exposure to asthma triggers. 2 Weather is a common triggering factor of asthma exacerbation.…”

Section: Introductionmentioning

confidence: 99%

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

Haque

Chai

et al. 2021

F1000Res

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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.

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Section: Introductionmentioning

confidence: 99%