Back to all available

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

epidemiology
public-health
data-mining-and-machine-learning
environmental-impacts
environmental-health

Abstract

Our research aimed to develop and validate a predictive analytics model for diagnosing sick building syndrome (SBS) in learners. We achieved this by gathering and analyzing epidemiological and exposure assessment data via a cross-sectional study approach. The current assessment involved the use of the modified MM040NA SBS questionnaire and checklist from Indoor Air Quality (IAQ) Industry Code of Practice (IAQ-ICOP) from the Department of Occupational Safety and Health (DOSH), Malaysia, with participants scoring their answers, and recording and scoring of their simultaneous self-reported and physician-ascertained health complaints. At the same time, IAQ assessments were collected at the location of each participant with the use of occupational hygiene techniques. Several predictive analytics algorithms, namely Neural Network, Logistic Regression, Classification Tree, Random Forest, and Support Vector Machine, were used to train and test the collected dataset. The Neural Network model rendered the most effective classification accuracy, reaching 82.5%. Validation also showed that multiple IAQ parameters were strongly associated with health complaints, especially in mechanically ventilated environments. Taken together, the results confirm the effectiveness of neural network-based predictive analytics in correctly diagnosing sick building syndrome (SBS) and related health complaints on limited IAQ data and thereby improving the ability to assess during the early stages.
Ask to review this manuscript

Notes for potential reviewers

  • Volunteering is not a guarantee that you will be asked to review. There are many reasons: reviewers must be qualified, there should be no conflicts of interest, a minimum of two reviewers have already accepted an invitation, etc.
  • This is NOT OPEN peer review. The review is single-blind, and all recommendations are sent privately to the Academic Editor handling the manuscript. All reviews are published and reviewers can choose to sign their reviews.
  • What happens after volunteering? It may be a few days before you receive an invitation to review with further instructions. You will need to accept the invitation to then become an official referee for the manuscript. If you do not receive an invitation it is for one of many possible reasons as noted above.
  • PeerJ does not judge submissions based on subjective measures such as novelty, impact or degree of advance. Effectively, reviewers are asked to comment on whether or not the submission is scientifically and technically sound and therefore deserves to join the scientific literature. Our Peer Review criteria can be found on the "Editorial Criteria" page - reviewers are specifically asked to comment on 3 broad areas: "Basic Reporting", "Experimental Design" and "Validity of the Findings".
  • Reviewers are expected to comment in a timely, professional, and constructive manner.
  • Until the article is published, reviewers must regard all information relating to the submission as strictly confidential.
  • When submitting a review, reviewers are given the option to "sign" their review (i.e. to associate their name with their comments). Otherwise, all review comments remain anonymous.
  • All reviews of published articles are published. This includes manuscript files, peer review comments, author rebuttals and revised materials.
  • Each time a decision is made by the Academic Editor, each reviewer will receive a copy of the Decision Letter (which will include the comments of all reviewers).

If you have any questions about submitting your review, please email us at [email protected].