Brain Tumor Prediction with Machine Learning

This review aims to systematically analyze ML models from four aspects: The investigation is based on the nurture of the ML technique, estimation precision, model evolution, and estimation setting. The systematic literature review of empirical studies focused on the published articles on ML models of the last decades. From the above sources, fifty-one primary studies concerning the objective of this research were identified on analyzing these studies; five ML techniques have been using it in the classification and prediction of brain tumors. the accuracies achieved by the estimated ML models can be consistories satisfactory, as they either match or surpass those of non-3fL models. The phenomenon of using improved machine learning models to classify and predict brain tumors him been the subject of numerous studies. Accenting to the mutters of the corresponding words, the genetic algorithm achieve‹t the highest level of prediction accuracy more than 10 among null the models that were tried. However, because of their complexity, these 3fL models have not yet been adopted by the industry and thus need more development.