Predictive Performance of Machine Learning Algorithms Regarding Obesity Levels Based on Physical Activity and Nutritional Habits: A Comprehensive Analysis
Keywords:
Artificial Neural Networks, Obesity, Machine LearningAbstract
Obesity is a complex chronic disease resulting from the interaction of multiple behavioral factors. This paper presents
the application of Machine Learning to identify the primary groups of behaviors contributing to the development of obesity.
Supervised machine learning emphasizes decision trees and deep artificial neural networks from datasets. The study also references
related work that utilizes predictive methods to estimate obesity levels based on physical activity and dietary habits. Furthermore,
it compares the performance of classification algorithms such as J48, Naive Bayes, Multiclass Classification, Multilayer Perceptron, KNN, and decision trees when predicting diabetes cases. The objective is to analyze different tools in the assessment based on physical activity and dietary habits, contributing to the improvement of obesity risk diagnosis. In addition, MLP and J48 demonstrated strong performance among all the algorithms, but BPTT achieved the highest overall performance.
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