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A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity.

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TLDR
In this paper, the authors conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020, and identified 93 papers from the review articles as primary studies from an initial pool of over 700 papers addressing obesity.
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This article is published in Computers in Biology and Medicine.The article was published on 2021-08-16 and is currently open access. It has received 75 citations till now. The article focuses on the topics: Public health & Overweight.

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Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review

TL;DR: In this paper , the authors discuss different research issues and possible research paths in which the aforementioned techniques might help materialize the smart city notion and offer a better understanding of the fundamentals of smart city and society management, the most recent developments and breakthroughs in this field, the benefits and drawbacks of existing methods, and areas that require further investigation and consideration.
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A Hybrid Deep Learning Model for Brain Tumour Classification

TL;DR: This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images to assist radiologists in tumour diagnostics using magnetic resonance imaging images without invasive procedures.
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Gut Microbiota: Therapeutic Targets of Ginseng Against Multiple Disorders and Ginsenoside Transformation

TL;DR: Overall, ginseng can regulate gut microbiome diversity, further affect the synthesis of secondary metabolites, as well as promote the transformation of ginsenosides for improving the absorptivity of gINSenosides.
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Anti-adipogenic activity of maackiain and ononin is mediated via inhibition of PPARγ in human adipocytes.

TL;DR: In this article , the anti-adipogenic capacity of Ononis spinosa L. roots (OSR) and its secondary metabolites ononin (ONON) and maackiain (MACK) was examined in an in vitro model of obesity.
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Anti-Diabesity Middle Eastern Medicinal Plants and Their Action Mechanisms

TL;DR: The aim of this review is to systematize published scientific data dealing with the efficiency of active ingredients or extracts from Middle Eastern medicinal plants and diet in the management of diabesity and its complications.
References
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Journal ArticleDOI

Health Effects of Overweight and Obesity in 195 Countries over 25 Years.

Ashkan Afshin, +167 more
TL;DR: The rapid increase in the prevalence and disease burden of elevated BMI highlights the need for continued focus on surveillance of BMI and identification, implementation, and evaluation of evidence‐based interventions to address this problem.
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The Epidemiology of Obesity: A Big Picture

TL;DR: The extent of the obesity epidemic, its risk factors—known and novel—, sequelae, and economic impact across the globe are discussed.
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Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.

TL;DR: The algorithms of machine learning, which can sift through vast numbers of variables looking for combinations that reliably predict outcomes, will improve prognosis, displace much of the work of radiologists and anatomical pathologists, and improve diagnostic accuracy.
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Predicting adult obesity from childhood obesity: a systematic review and meta‐analysis

TL;DR: Investigating the ability of simple measures of childhood obesity such as body mass index (BMI) to predict future obesity in adolescence and adulthood found that obese children and adolescents were around five times more likely to be obese in adulthood than those who were not obese.
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Trending Questions (1)
Can we improve the accuracy of body fat prediction by using gradient-enhanced machine learning algorithms?

Yes, the study identifies machine learning methods that can be used for the prediction of obesity, which suggests that gradient-enhanced machine learning algorithms could potentially improve the accuracy of body fat prediction.