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Four enhanced machine learning models were used to predict obesity in high school students by focusing on both risk and protective factors: binary logistic regression; improved decision tree (IDT); weighted k-nearest neighbor (KNN); and artificial neural network (ANN). Nine health-related behaviors from the 2015 Youth Risk Behavior Surveillance System (YRBSS) for the state of Tennessee were used as...
In upholding the Islamic way of life, effort to seek for moderation can be in the form of obesity prevention. Obesity is becoming the future burden of nations and actions have been taken to curb the problem of obesity. Most nations predict obesity based on the national past trend using data from population-based health surveys which are costly. Alternative method now points to data analytics which...
Artificial Neural Networks (ANNs) play a vital role in the medical field in solving various health problems like estimating the risk of cardiovascular diseases. The article concerns the process of developing ANNs for estimating the risk of arterial hypertension. ANNs proposed in this article use anthropometrical predictors, easy to control for everybody at home without special equipment. In the article...
Childhood is a dynamic period of life that rapid growth and development take place. Making mistakes in feeding practices of children may lead to undesirable consequences like malnutrition and obesity. Use of an automated computer-based technique to assist determination of daily dietary requirements of energy, protein and water has potential for preventing malnutrition and obesity. To get the solution...
Cooperations between engineers and physicians are crucial for studying and solving complex medical-biological problems. The study of obesity - unanimously regarded as a multi-factorial disease - is a typical example where specialists from various areas of medical research may be supported by engineers expert in system theory and software development. The effectiveness and the risk-benefit profile...
In this paper we present data mining and its utilization for childhood obesity prediction. Data mining was widely used in many childhood obesity prediction systems. Predicting obesity at an early age is both useful and important because the number of obese patients is increasing while its main cause cannot yet be defined. The ability to predict childhood obesity will help early prevention. The purpose...
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