Cancer has recently become a prevalent global health issue, according to research conducted by the WHO. Timely detection is crucial in preventing its spread, and treatment methods vary depending on the specific type of tumor present. Accurately classifying these tumors is essential for maximizing survival rates, which poses a significant challenge for researchers. Clinical data may sometimes be insufficient, and many tumors lack the morphological features necessary for classification. To obtain these features, a process known as feature selection is indispensable, particularly in the context of machine learning preprocessing. Feature selection involves choosing a subset of distinctive features that optimally reduces the feature space based on certain evaluation criteria. Utilizing an appropriate feature selection process can eliminate irrelevant data, enhance learning accuracy, and improve comprehensibility. This study introduces a rapid correlation-based filtering method for feature selection in conjunction with a linear Support Vector Machine (SVM). Furthermore, minor adjustments have been made to this approach to enhance its accuracy. A comparison is made between two different classifiers: back propagation as an artificial neural network and linear Support Vector Machine (SVM).