ABSTRACT
Machine learning is an artificial intelligence discipline that employs a variety of statistical, mathematical, and computational approaches to allow computers to “literate” through past instances and detect difficult-to-find trends in noisy, large, or complicated data sets. This capacity would’ve been beneficial to medical applications, particularly those which rely on advanced proteomic and genomic measures. As a result, machine learning has been widely utilised in the prediction and characterization of cancer. Furthermore, machine learning has also been used to identify and predict cancer. This technique is particularly fascinating even though it is part of a growing trend toward personalised, anticipatory treatment. During writing the above chapter, experts conducted a thorough examination of the many varieties of machine learning techniques that are employed, the sorts of information which are incorporated, and the effectiveness of these algorithms in prediction, prognosis, and diagnosis. There is a growing reliance on microarray data as well as nutrient biochemical markers, a sturdy prejudice forward into prostate and breast cancer application domains as well as a heavy reliance on “older” advanced technologies like convolutional neural networks (CNNs) as well as more recently developed and perhaps more easy and understandable machine learning based.
