A hybrid approach to obesity level determination with decision tree and pelican optimization algorithm

Abstract

Approximately 2 billion people in the world struggle with "obesity" and factors like eating lifestyle, habits, health conditions and mode of transport affect obesity. In this study, an artificial intelligence and machine learning-based model has been developed to predict obesity levels. It is proposed to create a hybrid model by combining the Decision Tree (DT) algorithm with the Pelican Optimization Algorithm (POA) on the obesity dataset of 2111 patients in SSggle. These models emphasize the critical role of parameters, aiming to achieve high performance. To solve the classification problem of multi-class obesity level determination, fuzzy logic-based parameter optimization is used to achieve high performance. While obesity rates are increasing worldwide, the study, which aims to globalize the parameters with the random discovery strategy of POA, is thought to be helpful for health professionals and decision-makers by successfully predicting obesity levels.

Keywords

• Artificial intelligence
• Obesity
• Machine learning
• Decision tree
• Pelican optimization algorithm
• Hybrid model

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