Intelligent Health Informatics with Personalisation in Weather-Based Healthcare Using Machine Learning

Haque, Radiah; Ho, Sin Ban; Chai, Ian; Teoh, Chin Wei; Abdullah, Adina; Tan, Chuie Hong; Dollmat, Khairi Shazwan

doi:10.1007/978-3-030-70713-2_4

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The WEA application also allows users to conduct the Asthma Control Test (ACT). 16 The ACT is a self-administered survey which is considered the standard assessment for monitoring chronic asthma and recommended by the Global Initiative for Asthma. 17 The ACT score is selected as the target output for prediction because it helps identify the severity and chances of asthma exacerbation.…”

Section: Data Collectionmentioning

confidence: 99%

“…17 The ACT score is selected as the target output for prediction because it helps identify the severity and chances of asthma exacerbation. 16 Data was collected through the WEA application from ten participants with asthma over a period of one-year. Participants conducted ACTs by regularly answering five multiple-choice questions, which include four asthma symptom-related questions and one asthma self-evaluate question.…”

Section: Data Collectionmentioning

confidence: 99%

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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: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

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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“…In machine learning, predicting student employability is considered an iterative process that includes gathering relevant data, cleaning and preparing the data, constructing models, validating them, and deploying the models for prediction [12]. Rather than considering all available student attributes as features, the prediction model focuses on selecting an optimal set (or a combination sets) of features that contribute to improving prediction performance in terms of accuracy [4].…”

Section: Introductionmentioning

confidence: 99%

Classification Techniques Using Machine Learning for Graduate Student Employability Predictions

Haque,

Quek,

Ting

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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The issue of employability has gained significant importance, not only for graduate students but also for higher educational institutions. In this regard, employability prediction models using machine learning have emerged as crucial techniques for assessing students' potential to secure employment after graduation. Enhancing university graduate employability is critical because student unemployment is a global concern that has widespread negative effects on both individuals and institutions. Therefore, focusing on graduate employability predictions using machine learning techniques is considered essential in addressing this issue. Traditionally, demographic and academic attributes, such as CGPA, have been considered key factors in determining student employment status. However, research suggests that various other factors, such as student satisfaction, might influence employability. This study employs machine learning techniques to identify the factors that affect graduate student employability. The objective is to investigate the features significantly influencing students' ability to secure employment. Data was collected from Malaysia's Ministry of Education's graduate tracer study (SKPG). Several classification algorithms were applied, including Logistic Regression, Random Forest, Naïve Bayes, Support Vector Machine, Extreme Gradient Boosting, and Artificial Neural Networks (ANN). The results show that ANN achieved the highest accuracy, with around 80%. The findings also revealed that student demographic and academic features and student satisfaction level with the university facilities (e.g., library and counseling service) are considered significant for graduate student employability predictions. Consequently, the empirical results can help higher educational institutions enhance facilities and prepare students with the necessary skills for future employability.

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Investigating the Impacts of Weather and Personalisation on Asthma Exacerbations using Machine Learning

Haque

Chai

et al. 2022

2022 11th International Conference on Software and Computer Applications

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Intelligent Health Informatics with Personalisation in Weather-Based Healthcare Using Machine Learning

Cited by 3 publications

References 13 publications

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

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

Classification Techniques Using Machine Learning for Graduate Student Employability Predictions

Investigating the Impacts of Weather and Personalisation on Asthma Exacerbations using Machine Learning

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