Applications of Machine Learning in Cancer Diagnosis and Prognosis
Rakesh Kumar, Sampurna Panda, Babita Panda, Naeem M. S. Hannoon · 2023
Many diverse subtypes of cancer have been described as a heterogeneous disease. Research into cancer has made early detection and prognosis essential since it can help doctors treat people more effectively. Many researchers from the field of biomedical and bioinformatics have turned to machine learning (ML) technologies because of the importance of categorizing the cancer patients into high- or low-risk category. So these techniques have been used to analyze the advancement and healing of cancer. ML automations are also important because of their ability to identify relevant features in complex datasets. ANNs, Bayesian Networks (BNs), support vector machines (SVMs), and decision trees (DTs) have been mostly used in cancer analysis for the construction of prediction models, leading to adequate and accurate predictions. In order to be considered in routine clinical practice, ML approaches must first be adequately validated before they can be used to increase our understanding of cancer progression. A survey of recent ML techniques used to model cancer progression is presented in this article. A variety of supervised ML approaches, predictor features, and datasets are used to create the predictive models described here. ML methods are increasingly being used in cancer research.