<![CDATA[Accurate Body Mass Index (BMI) prediction is essential for detecting obesity risks and related diseases. This study optimizes the Random Forest algorithm to enhance BMI prediction accuracy through hyperparameter tuning and feature selection. The dataset used is Obesity: Raw and Synthetic Data, which includes demographic and lifestyle variables. After undergoing subsetting, label encoding, and data imbalance handling using SMOTE, the model was trained using Random Forest and evaluated with accuracy, precision, recall, and F1-score metrics. The results indicate that the optimized model achieved 90% accuracy, with precision and recall of 0.89. Additionally, the feature importance analysis identified weight, height, and dietary habits as the most influential factors in BMI prediction. These findings confirm that optimizing the algorithm enhances model reliability in BMI classification and can be applied in data-driven health monitoring systems. This research is expected to contribute to the development of digital health applications and more accurate early obesity detection systems.]]>
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