BRAIN TUMOR PREDICTION WITH MACHINE LEARNING

Abstract

This review aims to systematically iinnlyze ML models from four aspects: The investigiition is based on the nurture of the ML technique, estimation precision, model evalu‹ition, anlt estimation setting. The systematic liter‹iture review of empirical studies focused on thepublished articles on ML models of the last decades. From the nbove sources, fifty-one primary studies concerning the objective of this research were i‹tentifieit On analyzing these studies, five ML techniques have been useit in the classification «nd prediction of brain tumors. the accuracies achieve‹t by the estimnted ML models can be consi‹tereit satisfactory, as they either match or surpass those of non-3fL models. The phenomenon of using improved machine learning models to cMssify and predict briiin tumors him been the subject of numerous studies. Accenting to the nutters of the corresponding words, the genetic algorithm achieve‹t the highest level of prediction accur‹ic more than 10  mong nll the models that were trie t. However, because of their complexity, these 3fL models have not yet been adopted by the industry ‹ind thus need more development