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

Siddiqui, Mohammad Khubeb ; Morales-Menendez, Ruben ; Ahmad, Sultan

Brazilian Archives of Biology and Technology, 2020-01, Vol.63 [Periódico revisado por pares]

Instituto de Tecnologia do Paraná (Tecpar)

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  • Título:
    Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity
  • Autor: Siddiqui, Mohammad Khubeb ; Morales-Menendez, Ruben ; Ahmad, Sultan
  • Assuntos: area under curve ; body mass index ; data mining ; obesity ; receiver operating characteristics ; support vector machine
  • É parte de: Brazilian Archives of Biology and Technology, 2020-01, Vol.63
  • Descrição: 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.
  • Editor: Instituto de Tecnologia do Paraná (Tecpar)
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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