Accurate forecasting and management are crucial as obesity represents a global health issue linked to many chronic diseases. This research examines the use of machine learning algorithms such as Decision Tree, Random Forest, K-NN (K Nearest Neighbor), and Naive Bayes for predicting obesity by using a carefully selected dataset that includes factors like height, weight, dietary choices, and activity levels. The data processing stage involved feature selection, normalizing values, and dealing with missing data to improve the performance of the models. Among all evaluated algorithms, the Decision Tree method outperformed the other algorithms with an accuracy of 98.33%, surpassing Random Forest (98.27%), K-NN (98.03%), and Naive Bayes (90.08%). The findings reveal that tree-based models are more effective for obesity classification than traditional BMI-based approaches. The results indicate that tree-based models perform more accurate classification for obesity than traditional BMI-based methods, which makes them good substitutes. This study demonstrates the potential utility of machine learning in better managing obesity and indicates points of potential improvement, including using more data sources and complex models to increase the accuracy of the predictions.
Zeeshan et al. (Fri,) studied this question.