A systematic literature review on outlier detection in wireless sensor networks

Safaei, Mahmood ORCID: 0000-0002-3924-6927, Sundararajan, Elankovan A., Driss, Maha, Boulila, Wadii and Shapi'i, Azrulhizam (2021) A systematic literature review on outlier detection in wireless sensor networks. Computers in Biology and Medicine, 136. pp. 104754-104771. doi:10.3390/sym12030328

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Abstract

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally.

Item Type: Article
Article Type: Article
Uncontrolled Keywords: Obesity; Overweight; Machine Learning; Risk Factors; Diseases
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Schools and Research Institutes > School of Computing and Engineering
Research Priority Areas: Applied Business & Technology
Depositing User: Kate Greenaway
Date Deposited: 16 Feb 2022 14:24
Last Modified: 16 Feb 2022 14:30
URI: https://eprints.glos.ac.uk/id/eprint/10686

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