Beyond 2020: Modelling obesity and diabetes prevalence

Ampofo, Ama Gyamfua; Boateng, Emmanuel B.

doi:10.1016/j.diabres.2020.108362

Cited by 94 publications

(92 citation statements)

References 34 publications

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“…Recently, the international diabetes prevention and control federation predicts that diabetes can affect more than 366 million people worldwide [49]. e disease control and prevention center in the US alarmed the government that diabetes can affect more than 29 million people [50].…”

Section: Role Of Physical Activity In Prevention and Control Of Diabetes Mellitusmentioning

confidence: 99%

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Butt

Letchmunan

Ali

et al. 2021

Journal of Healthcare Engineering

115

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The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.

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Section: Role Of Physical Activity In Prevention and Control Of Diabetes Mellitusmentioning

confidence: 99%

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Butt

Letchmunan

Ali

et al. 2021

Journal of Healthcare Engineering

115

View full text Add to dashboard Cite

show abstract

“…Epidemiological studies have reported a large increase in the prevalence of diabetes, which mainly happens among people with long-term abdominal obesity (Ampofo and Boateng, 2020).…”

Section: The Roles Of Lncrnas In Diabetic Complicationsmentioning

confidence: 99%

“…Epidemiological studies have reported a large increase in the prevalence of diabetes, which mainly happens among people with long-term abdominal obesity ( Ampofo and Boateng, 2020 ). The main harm of diabetes lies in its severe complications, such as diabetic retinopathy, nephropathy, cardiomyopathy, etc.…”

Section: The Potential Of Lncrnas As Therapeutic Targets Of Related Diseases Induced By Adipocyte Dysfunctionmentioning

confidence: 99%

Long Noncoding RNAs: Novel Important Players in Adipocyte Lipid Metabolism and Derivative Diseases

Zhang

Liu

et al. 2021

Front. Physiol.

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Obesity, a global public health issue, is characterized by excessive adiposity and is strongly related to some chronic diseases including cardiovascular diseases and diabetes. Extra energy intake-induced adipogenesis involves various transcription factors and long noncoding RNAs (lncRNAs) that control lipogenic mRNA expression. Currently, lncRNAs draw much attention for their contribution to adipogenesis and adipose tissue function. Increasing evidence also manifests the pivotal role of lncRNAs in modulating white, brown, and beige adipose tissue development and affecting the progression of the diseases induced by adipose dysfunction. The aim of this review is to summarize the roles of lncRNAs in adipose tissue development and obesity-caused diseases to provide novel drug targets for the treatment of obesity and metabolic diseases.

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“…The countries leading the ranking of obesity prevalence in the world are: China, USA, Brazil, India, Russia, Mexico, Germany, UK, Italy and France; and the countries leading the rank of severe obesity are: USA, China, Russia, Brazil, Mexico, Egypt, UK, Germany, Turkey and France (NCD Risk Factor, 2016). In addition, obesity estimates for 2030 antecipates an increse towards 42.1% of the populationin in the African continent, 45.5% in Asia, 36% in Europe, 44.5% in North America, 35.2% in South America and 65.8% in Oceania (Ampofo and Boateng, 2020).…”

Section: Introductionmentioning

confidence: 99%

The Weight of Obesity in Immunity from Influenza to COVID-19

Andrade

Gualberto

Rezende

et al. 2021

Front. Cell. Infect. Microbiol.

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged in December 2019 and rapidly outspread worldwide endangering human health. The coronavirus disease 2019 (COVID-19) manifests itself through a wide spectrum of symptoms that can evolve to severe presentations as pneumonia and several non-respiratory complications. Increased susceptibility to COVID-19 hospitalization and mortality have been linked to associated comorbidities as diabetes, hypertension, cardiovascular diseases and, recently, to obesity. Similarly, individuals living with obesity are at greater risk to develop clinical complications and to have poor prognosis in severe influenza pneumonia. Immune and metabolic dysfunctions associated with the increased susceptibility to influenza infection are linked to obesity-associated low-grade inflammation, compromised immune and endocrine systems, and to high cardiovascular risk. These preexisting conditions may favor virological persistence, amplify immunopathological responses and worsen hemodynamic instability in severe COVID-19 as well. In this review we highlight the main factors and the current state of the art on obesity as risk factor for influenza and COVID-19 hospitalization, severe respiratory manifestations, extrapulmonary complications and even death. Finally, immunoregulatory mechanisms of severe influenza pneumonia in individuals with obesity are addressed as likely factors involved in COVID-19 pathophysiology.

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Beyond 2020: Modelling obesity and diabetes prevalence

Cited by 94 publications

References 34 publications

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Long Noncoding RNAs: Novel Important Players in Adipocyte Lipid Metabolism and Derivative Diseases

The Weight of Obesity in Immunity from Influenza to COVID-19

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