Comparative Evaluation Of Machine Learning Classifiers For Brain Tumor Detection

Abstract This study evaluates the effectiveness of six machine learning classifiers—Support Vector Classifier (SVC), Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, and Random Forest—in detecting brain tumors using numerical data rather than traditional imaging techniques like MRI. The results emphasize the importance of data preprocessing, particularly feature scaling, in enhancing model performance. Among the classifiers, Random Forest emerged as the top performer, achieving an accuracy of 98.27% on both original and scaled data, demonstrating its robustness and reliability. The study highlights the potential of Random Forest as a valuable tool for automated brain tumor detection in clinical settings, offering a cost-effective and accessible alternative for resource-constrained environments. The paper suggests that future research should explore advanced deep learning models, such as 3D Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), to further improve diagnostic accuracy and support early intervention and personalized treatment strategies for brain tumor patients.