<![CDATA[Public health risks are often influenced by multiple lifestyle factors, such as age, diet, exercise, smoking, and alcohol consumption. This study aims to develop a predictive model for assessing individual health risks using the Support Vector Machine (SVM) algorithm. The dataset used consists of lifestyle attributes, including age, weight, height, exercise frequency, sleep duration, sugar intake, smoking habits, alcohol consumption, marital status, profession, and body mass index (BMI). The data were preprocessed through normalization and label encoding, followed by training and testing using a 70:30 data split. The SVM model employed the Radial Basis Function (RBF) kernel to capture non-linear relationships between variables. Experimental results show that the proposed SVM model achieved an accuracy of approximately 89%, demonstrating strong predictive capability. The confusion matrix analysis revealed that the model effectively distinguishes between high and low health risk categories, while the PCA visualization confirmed clear clustering of classified data. Moreover, the feature importance analysis indicated that age, smoking habits, BMI, and alcohol consumption were the most significant contributors to health risk prediction. Overall, the results suggest that the SVM algorithm is a robust and efficient approach for predicting public health risks based on lifestyle factors. This model can serve as a foundation for preventive health monitoring systems, providing valuable insights for promoting healthier lifestyles and supporting data-driven public health strategies.]]>
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