This paper compares the Support Vector Machine (SVM) and Discriminant Analysis (DA) in the classificationThis paper aims to compares the Support Vector Machine (SVM) and Discriminant Analysis (DA) in the classification process using a sample of 150 lung cancer patients due to accuracy, sensitivity, specificity ,and misclassification rate. The patients were divided into two groups: the first group consists of 101 patients with early-stage lung cancer, and the second group contains 49 patients with advanced-stage lung cancer. This classification was based on a set of independent variables: age (x1), smoking (x2), occupation (x3), COVID-19 infection (x4), and treatment methods (x5). The results advocate the superiority of SVM over Discriminant Analysis. That is, SVM was found to achieve an accuracy rate of 90.76%, sensitivity of 97%, and specificity of 77.5%, resulting in a misclassification rate of 9.33%. On the other hand, Discriminant Analysis accuracy rate is 70%, sensitivity is 70.29%, specificity is 69.4%, and a misclassification rate is 30%