COMPARATIVE STUDY ON OBESITY PREDICTION USING MACHINE LEARNING ALGORITHMS



Authors

  • Dr. S. Bharathidason1 and C. Sujdha2

DOI:

https://doi.org/10.15282/jmes.17.1.2023.10.0759


Keywords:

obesity, machine learning algorithms, k-nearest neighbor (k-NN), random forest, logistic regression, support vector machine (SVM), decision tree, Naïve Bayes(NB), Artificial Neural Network(ANN).


Abstract

In modern times, obesity has become a significant threat all over the world. Obesity means an unnatural or excessive amount of fat that is present in our bodies. People are constantly moving towards an unhealthy lifestyle, eating excessive junk food, late-night sleep, spend a long time sitting down. Especially, adolescents are being affected because of their unconscious attitudes. It is a medical problem known as a very complex disease. It promotes the spread of complex illnesses, stroke, heart disease, liver cancer. We have to move forward to prevent this risk of obesity. The purpose of this paper is to move towards a machine-learning-based pathway for predicting the risk of obesity using machine-learning algorithms. The great thing about this paper is that people will know the risk of obesity and the reasons behind their obesity. For this research, we apply seven prominent machine learning algorithms. We used the algorithm of k-nearest neighbor (k-NN), random forest(RF), logistic regression(LR), support vector machine (SVM), decision tree(DT), Naïve Bayes(NB), Artificial Neural Network(ANN). We found that the RF algorithm is more suitable for predicting the Obesity.



Published

2024-03-28

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