The uncontrolled growth of cells starts from a site in the human body and further spreads to other body parts known as cancer metastasis. Cancer was introduced to the medical world in the 1600 s and is associated with abnormally growing cells that can invade or spread to other parts of the body. The word cancer comes from the ancient Greek kapkivoc, which means crab and tumor. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Five investigations have been designed, and solutions to those were explored.
In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 20. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Artificial intelligence has aided in the advancement of healthcare research.
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