MODEL PREDIKSI FAKTOR-FAKTOR RISIKO OBESITAS MENGGUNAKAN MACHINE LEARNING - Husnul Khuluq; Lazuardi Fatahillah Hamdi; Ayu Nissa Ainni; Tri Cahyani Widiastuti (Departemen of Health Policy and Management, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta Jl. Farmako Sekip Utara Yogyakarta 55281 Telp 0274-547490, 2026)
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: