AbstractThe application of machine learning (ML) techniques has led to notable breakthroughs in oncology, the study and treatment of cancer, especially in the field of early cancer diagnosis. By detecting cancer at its most curable stages, early detection is essential for increasing survival rates and treatment results. This study examines how machine learning models can be used to analyse patient data, such as symptoms, genetic information, and medical histories, to predict and diagnose cancer in its early stages. When processing huge, complicated datasets to find patterns and forecast cancer risk, machine learning methods including logistic regression, random forests, support vector machines, and gradient boosting have demonstrated encouraging results. Compared to conventional techniques, machine learning (ML) in cancer offers the advantages of speedy analysis of large volumes of data, the capacity to spot hidden trends, and more precise predictions. There are still issues to be resolved, though, such as managing data imbalance, making sure models are interpretable, and the requirement for strong, varied datasets to improve generalizability. Obstacles to integrating machine learning algorithms into clinical practice also include ethical considerations, regulatory issues, and the requirement for physician collaboration. Notwithstanding these obstacles, machine learning in oncology has a bright future ahead of it in terms of enhancing early cancer detection, cutting medical expenses, and facilitating individualized treatment plans. With an emphasis on how machine learning could transform cancer diagnosis and treatment, this study explores the field’s present uses as well as anticipated future developments.
Gupta et al. (Wed,) studied this question.