Application of machine learning algorithms in building health diagnostics: predictive analytics evaluating indoor air quality and sick building syndrome in educational settings

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dc.contributorUniversiti Sains Malaysia
dc.contributor.authorIsmail, Syazwan Aizat Ismail
dc.date.accessioned2026-08-31T20:21:40Z
dc.date.available2026-08-31T20:21:40Z
dc.descriptionThis dataset contains epidemiological and exposure assessment data used to develop and validate a predictive analytics model for diagnosing sick building syndrome (SBS) in learners. It includes responses from a modified MM040NA SBS questionnaire alongside simultaneous indoor air quality (IAQ) physical parameters, demonstrating the application of neural networks in classifying IAQ health risks.
dc.identifierUTMPR Grant Vote: 00L45
dc.identifier.urihttps://zenodo.org/records/17613912
dc.identifier.urihttps://opendata.usm.my/handle/123456789/74745
dc.language.isoen
dc.relation.ispublishedinhttps://peerj.com/articles/20964/
dc.relation.titleDataset Sick Building Syndrome ML Prediction
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEpidemiology
dc.subjectPublic Health
dc.subjectData Mining
dc.subjectMachine Learning
dc.subjectEnvironmental Impacts
dc.subjectEnvironmental Health
dc.titleApplication of machine learning algorithms in building health diagnostics: predictive analytics evaluating indoor air quality and sick building syndrome in educational settings
dc.typeDataset
oaire.fundingStreamUniversiti Teknologi Malaysia
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