Obesity Prediction Using Machine Learning Algorithms
DOI:
https://doi.org/10.57152/ijatis.v2i1.1869Keywords:
Decision Tree, K-NN, NBC, Random Forest, SVM, Obesity PredictionAbstract
This study aims to develop a prediction model for obesity levels by utilizing five machine learning algorithms, namely K-Nearest Neighbors (K-NN), Naïve Bayes Classifier (NBC), Decision Tree, Random Forest, and Support Vector Machine (SVM). The data used in this study were obtained from Kaggle, consisting of 2111 data with 17 attributes covering lifestyle and demographic factors. The research process involved data collection, pre-processing, data division using the Holdout Split method (70% training data and 30% testing data), and the application of machine learning algorithms. Performance evaluation used accuracy, precision, recall, and F1 score metrics. The results showed that the Random Forest algorithm had the best performance with an accuracy of 92.29%, followed by Decision Tree at 90.54%, K-NN at 83.44%, and NBC and SVM which reached 59.15% and 59.08%, respectively. Confusion matrix analysis revealed that NBC and SVM had difficulty distinguishing certain obesity classes. Based on these findings, it can be concluded that Random Forest is the most effective algorithm in predicting obesity levels. The results of this study are expected to contribute to developing a more accurate obesity prediction system that can be implemented in the real world.
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