Implementation of SVM in Predicting Obesity Risk Based on Lifestyle and Dietary Patterns - Adinda Febiola; Fahriya Ardiningrum; Michael Orlando A. Purba; Fernando Siahaan; Victor Asido Elyakim P (Yayasan Literasi Sains Indonesia, 2025)
Obesity is one of the global health issues that has seen a significant increase in recent decades. This condition is closely related to an unbalanced modern lifestyle, such as lack of physical activity, unhealthy eating patterns, and habits of smoking and alcohol consumption. This study aims to analyze the relationship between lifestyle and obesity risk, as well as to evaluate the effectiveness of the Support Vector Machine (SVM) method in predicting the level of obesity risk. The dataset used was obtained from the Kaggle platform, covering various variables such as age, gender, body mass index (BMI), eating habits, sleep patterns, and physical activity. Preprocessing was carried out through data normalization and encoding of categorical variables to ensure data readiness before being input into the model. The SVM model was trained using various training and testing data split ratios and showed a very high accuracy rate, even reaching 100% in some scenarios. These results demonstrate that SVM can effectively identify patterns in lifestyle data that contribute to obesity. Thus, the application of SVM can be a useful predictive tool for healthcare professionals in designing more accurate and efficient data-driven obesity prevention strategies.