<![CDATA[Background: Obesity is a major global health concern and a key risk factor for various non-communicable diseases, including diabetes, hypertension, and cardiovascular disorders. Despite extensive studies, accurately identifying the key contributing factors remains a challenge. Objective: This study aims to predict the likelihood of obesity using a machine learning algorithm, based on questionnaire-derived clinical and behavioral data. Several supervised machine learning algorithms—logistic regression, naïve Bayes, support vector machine (SVM), and random forest—will be employed to build predictive models. Model performance will be evaluated using accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Methods: We used an open-access dataset from Kaggle comprising 2,111 samples with anthropometric, demographic, and lifestyle data. Of these, 972 individuals were categorized as obese and 1,139 as non-obese. The target variable was categorized into binary labels: "Obesity" and "Non-Obesity." Preprocessing included one-hot encoding, label encoding, and train-test splitting. All four ML models were trained and evaluated using accuracy, area under the curve (AUC), precision, sensitivity, and specificity metrics. Results: The model achieved an accuracy of 98.58%, AUC of 99.96%, sensitivity of 98.99%, specificity of 98.21%, and precision of 98.01%. The most influential predictors were weight, frequent consumption of high-caloric food, family history of being overweight, physical activity frequency, and daily water intake. Conclusion: The model demonstrated high performance and identified key lifestyle-related features. These findings support machine learning's potential for obesity screening and public health strategy development.]]>
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