Machine Learning-Based Asthma Risk Prediction Using IoT and Smartphone Applications
DC Field | Value | Language |
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dc.contributor.author | Bhat, Gautam S. | - |
dc.contributor.author | Shankar, Nikhil | - |
dc.contributor.author | Kim, Dohyeong | - |
dc.contributor.author | Song, Dae Jin | - |
dc.contributor.author | Seo, Sungchul | - |
dc.contributor.author | Panahi, Issa M. S. | - |
dc.contributor.author | Tamil, Lakshman | - |
dc.date.accessioned | 2022-03-11T21:40:18Z | - |
dc.date.available | 2022-03-11T21:40:18Z | - |
dc.date.created | 2022-01-20 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/138640 | - |
dc.description.abstract | In this paper, we present an asthma risk prediction tool based on machine learning (ML). The entire tool is implemented on a smartphone as a mobile-health (m-health) application using the resources of Internet-of-Things (IoT). Peak Expiratory Flow Rates (PEFR) are commonly measured using external instruments such as peak flow meters and are well known asthama risk predictors. In this work, we find a correlation between the particulate matter (PM) found indoors and the outside weather with the PEFR. The PEFR results are classified into three categories such as 'Green' (Safe), 'Yellow' (Moderate Risk) and 'Red' (High Risk) conditions in comparison to the best peak flow value obtained by each individual. Convolutional neural network (CNN) architecture is used to map the relationship between the indoor PM and weather data to the PEFR values. The proposed method is compared with the state-of-the-art deep neural network (DNN) based techniques in terms of the root mean square and mean absolute error accuracy measures. These performance measures are better for the proposed method than other methods discussed in the literature. The entire setup is implemented on a smartphone as an app. An IoT system including a Raspberry Pi is used to collect the input data. This assistive tool can be a cost-effective tool for predicting the risk of asthma attacks. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | INDOOR AIR-QUALITY | - |
dc.subject | SPEECH ENHANCEMENT | - |
dc.subject | DISEASE PREDICTION | - |
dc.subject | NETWORK | - |
dc.subject | HEALTH | - |
dc.subject | HEARING | - |
dc.subject | SEOUL | - |
dc.title | Machine Learning-Based Asthma Risk Prediction Using IoT and Smartphone Applications | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Song, Dae Jin | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3103897 | - |
dc.identifier.scopusid | 2-s2.0-85114312459 | - |
dc.identifier.wosid | 000692177700001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.9, pp.118708 - 118715 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 9 | - |
dc.citation.startPage | 118708 | - |
dc.citation.endPage | 118715 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | INDOOR AIR-QUALITY | - |
dc.subject.keywordPlus | SPEECH ENHANCEMENT | - |
dc.subject.keywordPlus | DISEASE PREDICTION | - |
dc.subject.keywordPlus | NETWORK | - |
dc.subject.keywordPlus | HEALTH | - |
dc.subject.keywordPlus | HEARING | - |
dc.subject.keywordPlus | SEOUL | - |
dc.subject.keywordAuthor | Respiratory system | - |
dc.subject.keywordAuthor | Atmospheric modeling | - |
dc.subject.keywordAuthor | Meteorology | - |
dc.subject.keywordAuthor | Diseases | - |
dc.subject.keywordAuthor | Convolutional neural networks | - |
dc.subject.keywordAuthor | Real-time systems | - |
dc.subject.keywordAuthor | Predictive models | - |
dc.subject.keywordAuthor | Asthma prediction | - |
dc.subject.keywordAuthor | particulate matter (PM) | - |
dc.subject.keywordAuthor | peak expiratory flow rates (PEFR) | - |
dc.subject.keywordAuthor | Internet-of-Things (IoT) | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | Raspberry Pi | - |
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