CLASSIFICATION OF CANCEROUS PROFILE BASED ON TYPE, STAGE AND HETEROGINITY USING MACHINE LEARNING

Abstract - Cancer is a disease characterized by the abnormal growth and division of cells, which can invade and damage surrounding tissues and organs. It begins when normal cells start to grow and divide uncontrollably due to mutations in genes that control cell growth and division, exposure to certain chemicals or radiation, or a weakened immune system. This survey paper sights to provide an all inclusive analysis of recent studies on the classification of cancerous profiles using machine learning techniques. The paper focuses on the classification of cancerous profiles based on various parameters, including the type, stage, and heterogeneity of cancer. The paper presents an overview of the different types of cancers and the challenges associated with their diagnosis and treatment. The paper then reviews various machine learning algorithms used in cancer classification and highlights their strengths and limitations. Keywords - Cancer, Support Vector Machines, Random Forest, k-nearest neighbors (k-NN), LightGBM (LGBM) algorithm, gradient boosted classifier, Convolutional Neural Network (CNN)