Deciphering Cancer's Code: A Review on Advanced Machine Learning Approaches in DNA-Based Cancer Detection

Abstract

This review provides a comprehensive synthesis of the latest developments in cancer detection and classification through the application of deep learning and machine learning techniques. An extensive range of methodologies is thoroughly assessed in the paper, encompassing Principal Component Analysis, Singular Value Decomposition, Autoencoders, Deep Belief Networks, Convolutional Neural Networks, and numerous architectures for cancer detection. The clinical implications and transformative potential of these computational approaches to improve the accuracy and efficacy of cancer diagnosis are highlighted. The paper presents an orderly supposition of the investigations conducted in the field, wherein each study introduced distinct approaches and methodologies aimed at augmenting early cancer detection and educating approaches to treatment. Although the results are encouraging, the article stresses the importance of conducting more extensive validations on a wide range of patient populations and investigating potential synergies with complementary technologies. The results obtained from these investigations represent substantial advancements in the field of biomedical informatics, offering innovative approaches that have the potential to revolutionize personalized medicine and healthcare.