An Early Detection of Asthma Using BOMLA Detector

Awal, Md. Abdul; Hossain, Md. Shahadat; Debjit, Kumar; Ahmed, Nafiz; Nath, Rajan Dev; Habib, G M Monsur; Khan, Md. Salauddin; Islam, Md. Akhtarul; Mahmud, M. A. Parvez

doi:10.1109/access.2021.3073086

Cited by 16 publications

(7 citation statements)

References 53 publications

(51 reference statements)

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“…Step 1: Using Oversampling method for converting imbalanced dataset to balanced dataset [15]. Dataset balancing was done by oversampling technique SMOTE [16] and ADASYN [17]- [20]. SMOTE The original imbalanced dataset had total 70943 records.…”

Section: Methodsmentioning

confidence: 99%

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Machine learning ensemble approach for healthcare data analytics

Javale

Desai²

2022

IJEECS

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In healthcare machine learning is used mainly for disease diagnosis or acute condition detection based on patient data analysis. In the proposed work diabetic patient dataset analysis is done for hypoglycemia detection which means the lowering of blood glucose level. Often in healthcare it is observed that the dataset is imbalanced. Therefore an Ensemble Approach using imbalanced dataset techniques Synthetic Minority Over-sampling Technique and Adaptive Synthetic oversampling methods with different evaluation methods like train-test, k-fold, Stratified K-Fold and repeat train-test were used. This ensemble approach was implemented on diabetic dataset using K-Nearest Neighbor, Support Vector Machine, Random Forest, Naïve Bayes and Logistic Regression classifiers with average Stacking-C method thereafter to conclude. Comparative analysis was done using three different considerations. The results showed that KNN and Random forest gives more stable metric values both on balanced and imbalanced dataset. The confusion matrix consideration concluded that KNN and Random Forest were found to be better with least false negative and maximum true positive count. But if average train and test time is taken into consideration then Naïve Bayes and Random forest had least average train-test time. Thus the three different considerations concluded that the proposed ensemble approach gives better clarity for different classifier implementation using machine learning.

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Section: Methodsmentioning

confidence: 99%

“…The number of records for disease diagnosed is less than the number of records where disease is not diagnosed. Therefore, an Ensemble approach was used for Hypoglycemia detection analysis using superficial body parameter readings [16], [17].…”

Section: Ensemble Approach Usedmentioning

confidence: 99%

Machine learning ensemble approach for healthcare data analytics

Javale

Desai²

2022

IJEECS

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“…After the preprocessing, mainly three classification algorithms were applied: XGB, RF, and SVM. These classifiers provide improved results in many studies [ 21 , 22 , 23 ]. Furthermore, studies found significantly improved results by applying XGB in COVID-19 mortality prediction and prepared a clinically operable Covid decision support system using XGB for clinical staff [ 23 , 24 ].…”

Section: Analysis Proceduresmentioning

confidence: 99%

Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage

Raihan¹,

Hassan²,

Hasan³

et al. 2022

Bioengineering

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COVID-19 has imposed many challenges and barriers on traditional healthcare systems due to the high risk of being infected by the coronavirus. Modern electronic devices like smartphones with information technology can play an essential role in handling the current pandemic by contributing to different telemedical services. This study has focused on determining the presence of this virus by employing smartphone technology, as it is available to a large number of people. A publicly available COVID-19 dataset consisting of 33 features has been utilized to develop the aimed model, which can be collected from an in-house facility. The chosen dataset has 2.82% positive and 97.18% negative samples, demonstrating a high imbalance of class populations. The Adaptive Synthetic (ADASYN) has been applied to overcome the class imbalance problem with imbalanced data. Ten optimal features are chosen from the given 33 features, employing two different feature selection algorithms, such as K Best and recursive feature elimination methods. Mainly, three classification schemes, Random Forest (RF), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM), have been applied for the ablation studies, where the accuracy from the XGB, RF, and SVM classifiers achieved 97.91%, 97.81%, and 73.37%, respectively. As the XGB algorithm confers the best results, it has been implemented in designing the Android operating system base and web applications. By analyzing 10 users’ questionnaires, the developed expert system can predict the presence of COVID-19 in the human body of the primary suspect. The preprocessed data and codes are available on the GitHub repository.

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“…Awal et al [ 114 ] applied multiple learning models to investigate the parameters that characterize asthma diagnosis and prediction. BOMLA (Bayesian Optimisation-based Machine Learning Framework for Asthma), a new machine learning technique, had been developed for identifying asthma.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Koul

Bawa²,

Kumar³

2022

Arch Computat Methods Eng

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Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.

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An Early Detection of Asthma Using BOMLA Detector

Cited by 16 publications

References 53 publications

Machine learning ensemble approach for healthcare data analytics

Machine learning ensemble approach for healthcare data analytics

Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

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