Brain Tumor Classification in Sustainable Healthcare using Machine Learning

In sustainable healthcare, early and accurate diagnosis is crucial in reducing the burden on healthcare systems and improving patient outcomes. This paper explores the application of machine learning techniques, specifically K-Nearest Neighbors (KNN) and Support Vector Classifier (SVC), for classifying brain tumors based on medical imaging data. By leveraging these machine learning methods, we aim to provide an efficient and interpretable solution that maintains high accuracy while optimizing computational resources. Machine learning models, due to their lower resource demands, are more suitable than deep learning approaches for sustainable healthcare environments, particularly in scenarios with limited infrastructure. The proposed models are validated through cross validation and hyperparameter tuning to achieve optimal performance. Our results demonstrate the potential of machine learning in brain tumor classification, offering a balance between accuracy and sustainability, and paving the way for more scalable and accessible diagnostic systems.