Predicting of cancer disease using machine learning models, a review

Abstract

Cancer is a serious disease that leads to mortality if not exposed at a precocious phase. This is what makes it worrying, as no actual treatment has been found for this disease yet, and patients with the disease cannot be saved unless it is diagnosed early in the first and second stages. But, if it is discovered in the third and fourth stages, the patient has little chance of survival. This is why using machine learning is a beneficial technology to deal with these diseases to help in their early detection. Machine learning models effectively learn from a training dataset how to estimate entire processes. Many models are offered by machine learning to forecast cancer based on standard datasets gathered by healthcare websites, social media, and some other repositories. That is why there is an urgent need to apply machine learning algorithms to this dataset for early cancer detection in affected people. The essential goal of the review is to highlight and estimate the best approaches used and make it easier for other researchers to conduct relevant studies utilizing machine learning models on cancer. The survey indicates that the models are competitive and demonstrate the accuracy of the classifiers on the datasets they were trained on.