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      Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity

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          Abstract

          Abstract Obesity is the most common chronic disease, due to its ignorance in society. It gives birth to other diseases such as endocrine. The objective of this research is to analyze the different trends of each BMI category and predict its related serious consequences. Data mining based Support Vector Machine (SVM) technique has been applied for this and the accuracy of each BMI category has been calculated using Receiver Operating Characteristics (ROC), which is an effective method and potentially applied to medical data sets. The Area Under Curve (AUC) of ROC and predictive accuracy have been calculated for each classified BMI category. Our analysis shows interesting results and it is found that BMI ≥ 25 has the highest AUC and Predictive accuracy compares to other BMI, which claims a good rank of performance. From our trends, it has been explored that at each BMI precaution is mandatory even if the BMI < 18.5 and at ideal BMI too. Development of effective awareness, early monitoring and interventions can prevent its harmful effects on health.

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          Most cited references43

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          The medical risks of obesity.

          Obesity is at epidemic proportions in the United States and in other developed and developing countries. The prevalence of obesity is increasing not only in adults, but especially among children and adolescents. In the United States in 2003 to 2004, 17.1% of children and adolescents were overweight, and 32.2% of adults were obese. Obesity is a significant risk factor for and contributor to increased morbidity and mortality, most importantly from cardiovascular disease (CVD) and diabetes, but also from cancer and chronic diseases, including osteoarthritis, liver and kidney disease, sleep apnea, and depression. The prevalence of obesity has increased steadily over the past 5 decades, and obesity may have a significant impact on quality-adjusted life years. Obesity is also strongly associated with an increased risk of all-cause mortality as well as cardiovascular and cancer mortality. Despite the substantial effects of obesity, weight loss can result in a significant reduction in risk for the majority of these comorbid conditions. Those comorbidities most closely linked to obesity must be identified to increase awareness of potential adverse outcomes. This will allow health care professionals to identify and implement appropriate interventions to reduce patient risk and mortality. A systematic search strategy was used to identify published literature between 1995 and 2008 that reported data from prospective longitudinal studies of obesity and comorbid medical conditions. This article will review evidence for significant associations of obesity with comorbidities to provide information useful for optimal patient management.
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            Application of Deep Learning for Fast Detection of COVID-19 in X-Rays using nCOVnet

            Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.
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              The epidemiology of obesity.

              Obesity has received considerable attention as a major health hazard because of the increase in the prevalence of obesity not only in the United States but also in several other countries worldwide. Obesity is caused by an interaction of environmental factors, genetic predisposition, and human behavior, and is associated with an increased risk of numerous chronic diseases, from diabetes and cancers to many digestive diseases. The obesity epidemic exerts a heavy toll on the economy with its massive health care costs. This article describes some of the epidemiologic features of obesity, including global prevalence, secular trends, risk factors, and burden of illness related to obesity with special emphasis on obesity trends in the United States. Published by Elsevier Inc.
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                Author and article information

                Journal
                babt
                Brazilian Archives of Biology and Technology
                Braz. arch. biol. technol.
                Instituto de Tecnologia do Paraná - Tecpar (Curitiba, PR, Brazil )
                1516-8913
                1678-4324
                2020
                : 63
                : e20190736
                Affiliations
                [2] Alkharj Riyadh orgnamePrince Sultan University orgdiv1College of Computer Engineering and Sciences orgdiv2Department of Computer Science Saudi Arabia
                [1] Monterrey orgnameTecnológico de Monterrey orgdiv1School of Engineering and Sciences Mexico
                Article
                S1516-89132020000100316 S1516-8913(20)06300000316
                10.1590/1678-4324-2020190736
                79f27210-81ca-4430-8b72-93f50db0856b

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

                History
                : 30 March 2020
                : 21 December 2019
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 72, Pages: 0
                Product

                SciELO Brazil

                Self URI: Full text available only in PDF format (EN)
                Categories
                Article - Human and Animal Health

                area under curve,obesity,body mass index,receiver operating characteristics,support vector machine,data mining

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