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

Haque, Radiah; Ho, Sin Ban; Chai, Ian; Abdullah, Adina

doi:10.12688/f1000research.73026.1

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…This is typically done by identifying the True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) for each prediction class [32]. From the confusion matrix, equations 1-4 are used for calculating the evaluation metrics [33]:…”

Section: B Classification Modelsmentioning

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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Section: B Classification Modelsmentioning

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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“…In this study [15], the authors address the existing gap in mHealth applications for asthma self-management by proposing an optimized Deep Neural Network Regression (DNNR) model. Integrating weather, demography, and asthma tracking, the model demonstrates significant potential, achieving a score of 0.83 with Mean Absolute Error (MAE) of 1.44 and Mean Squared Error (MSE) of 3.62.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Airborne Disease Prediction: Integrating Deep Infomax and Self-Organizing Maps for Risk Factor Identification

Pimpale,

Pandit

2023

IJACSA

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Asthma poses a significant global public health concern, particularly in urban centers where environmental pollutants and variable weather patterns contribute to heightened prevalence and symptom exacerbation. The Deonar dumping ground, one of Mumbai's largest landfills, releases a complex mix of particulate matter and hazardous gases, posing a serious threat to local respiratory health. Despite the urgency for comprehensive research integrating patient-specific data with localized weather and air quality metrics, such studies remain limited. This study addresses the critical research gap by investigating asthma risk factors near the Deonar dumping ground. Integrating detailed patient records with precise local weather and air quality measurements, our research aims to unravel the intricate relationship between environmental exposure and respiratory health outcomes. The findings provide crucial insights into the specific risk factors influencing asthma incidence and severity in this region, informing the development of targeted interventions and mitigation strategies. Employing a novel ensemble Deep Info Max -Self-Organizing Map (DIM-SOM) technique, our study compares its performance with various clustering algorithms, including SOM, K-Means, Bisecting K-Means, DBSCAN, and others. The novel ensemble DIM-SOM demonstrated superior performance, achieving a significantly higher Silhouette Score of 0.9234, a lower Davies-Bouldin Score of 0.1276, and a more favorable Calinski-Harabasz Score of 389723.6225 compared to other algorithms. These findings underscore the efficacy of the novel ensemble DIM-SOM approach in generating dense, wellseparated, and meaningful clusters, emphasizing its potential to enhance clustering performance compared to traditional algorithms. The study further emphasizes the need for proactive mitigation measures and tailored healthcare interventions based on the identified environmental risk factors.

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“…The same features (except for gender) were replicated using a generalized linear model [ 68 ]. In addition, Haque et al used a DNN regression (DNNR) model to predict asthma exacerbations on the basis of Asthma Control Test (ACT) scores, weather triggers (temperature, humidity, pressure, and windspeed) and demographic data, with their approach achieving an accuracy of 94% [ 69 ].…”

Section: Studies Using Machine Learning To Predict Asthma Exacerbationsmentioning

confidence: 99%

Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review

Molfino,

Turcatel,

Riskin

2023

Adv Ther

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The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent years, especially as a result of their potential to revolutionize personalized medicine. Despite advances in the treatment and management of asthma, a significant proportion of patients continue to suffer acute exacerbations, irrespective of disease severity and therapeutic regimen. The situation is further complicated by the constellation of factors that influence disease activity in a patient with asthma, such as medical history, biomarker phenotype, pulmonary function, level of healthcare access, treatment compliance, comorbidities, personal habits, and environmental conditions. A growing body of work has demonstrated the potential for AI and ML to accurately predict asthma exacerbations while also capturing the entirety of the patient experience. However, application in the clinical setting remains mostly unexplored, and important questions on the strengths and limitations of this technology remain. This review presents an overview of the rapidly evolving landscape of AI and ML integration into asthma management by providing a snapshot of the existing scientific evidence and proposing potential avenues for future applications.

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

Cited by 8 publications

References 19 publications

Classification Techniques Using Machine Learning for Graduate Student Employability Predictions

Classification Techniques Using Machine Learning for Graduate Student Employability Predictions

Enhancing Airborne Disease Prediction: Integrating Deep Infomax and Self-Organizing Maps for Risk Factor Identification

Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review

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