This research aims to enhance the effectiveness of lung cancer classification using Support Vector Machines (SVM) with hyperparameter tuning. The study uses Radial Basis Function (RBF) kernels in SVM to handle non-linear problems and employs Random Grid Search to find the optimal hyperparameters. The best parameter settings are C = 10, Gamma = 10, and Probability = True. Test results show that the tuned SVM significantly improves accuracy, precision, specificity, and F1 score, with a slight decrease in recall. The study emphasizes the importance of both recall and specificity in disease classification, especially in imbalanced datasets. The proposed method achieves an accuracy of 0.99, precision of 1.00, recall of 0.98, F1-score of 0.99, and specificity of 1.00. The research confirms the potential of tuned SVMs in addressing complex data classification challenges and provides insights for medical diagnostic applications. The study compares SVM with other machine learning models like KNN, LR, RF, GB, and LGBM, showing that SVM with hyperparameter tuning performs best. The study also applies random oversampling to balance the dataset, but finds that it does not significantly improve performance compared to hyperparameter tuning. The results demonstrate the effectiveness of hyperparameter tuning in improving classification accuracy and model performance for lung cancer detection.This research aims to enhance the effectiveness of lung cancer classification using Support Vector Machines (SVM) with hyperparameter tuning. The study uses Radial Basis Function (RBF) kernels in SVM to handle non-linear problems and employs Random Grid Search to find the optimal hyperparameters. The best parameter settings are C = 10, Gamma = 10, and Probability = True. Test results show that the tuned SVM significantly improves accuracy, precision, specificity, and F1 score, with a slight decrease in recall. The study emphasizes the importance of both recall and specificity in disease classification, especially in imbalanced datasets. The proposed method achieves an accuracy of 0.99, precision of 1.00, recall of 0.98, F1-score of 0.99, and specificity of 1.00. The research confirms the potential of tuned SVMs in addressing complex data classification challenges and provides insights for medical diagnostic applications. The study compares SVM with other machine learning models like KNN, LR, RF, GB, and LGBM, showing that SVM with hyperparameter tuning performs best. The study also applies random oversampling to balance the dataset, but finds that it does not significantly improve performance compared to hyperparameter tuning. The results demonstrate the effectiveness of hyperparameter tuning in improving classification accuracy and model performance for lung cancer detection.