Abstract
The aim of this research work is the early prediction of childhood obesity after the age of three years from available clinical records of patients. Nowadays, child obesity is a highlighted research area as excessive body fat harmfully affects a child’s health. Obese children have more risk of suffering from health problems such as heart diseases, type 2 diabetes, cancer, and osteoarthritis in their adulthood. Thus, early prediction of childhood obesity is essential for fat and overweight babies. In this paper, we have proposed a prediction model for this purpose. Analyses of three different machine learning methods: SVM, KNN, and ANN for establishing accuracy in the prediction model have been done. From the result analysis, it can be established that a prediction model based on machine learning techniques can be used to predict obesity in children after the age of two years.
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Chatterjee, K., Jha, U., Kumari, P., Chatterjee, D. (2021). Early Prediction of Childhood Obesity Using Machine Learning Techniques. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds) Advances in Communication and Computational Technology. ICACCT 2019. Lecture Notes in Electrical Engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_109
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DOI: https://doi.org/10.1007/978-981-15-5341-7_109
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