Review on Multiple Cancer Disease Prediction And Identification using Machine Learning Techniques

Abstract: Cancer has been described as a diverse illness with a wide range of subgroups. Early cancer diagnosis and prognosis are essential for clinical patient treatment, which has become a requirement in cancer research. Numerous research teams from the biomedical and bioinformatics fields have studied the use of machine learning (ML) techniques due to the significance of categorising cancer patients into high or low risk groups. These methods have been applied in an effort to simulate the development and management of malignant diseases. Additionally, the importance of ML tools is demonstrated by their capacity to recognise important elements in complex datasets. Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs) are a few of the methods that have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although it is clear that applying ML techniques can enhance our comprehension of how cancer progresses, more validation is required before these techniques can be used in routine clinical practise. Advance ML techniques are used in this work to mimic the detection of cancer in numerous human bodily organs, including the brain, breast, and lung.