SHAPING THE FUTURE: A LITERATURE REVIEW ON MACHINE LEARNING APPLICATIONS FOR PREDICTING CANCER RECURRENCE
A thorough summary of the possibilities for improving cancer recurrence prediction with machine learning (ML) is given in this current review of the research. Numerous research analyzed demonstrate the effectiveness of various machine learning algorithms and methodologies in precisely detecting patterns and forecasting the likelihood of a cancer recurrence. Even with these developments, this field still has opportunities for research and development. Future studies should focus on utilizing deep learning models and ensemble techniques—two recent developments in machine learning—to increase the prediction capacity and generalizability of these methods. Furthermore, the construction of complete predictive models requires the integration of increasingly varied information and the evaluation of other clinical aspects. The revolutionary effect of machine learning (ML) in predicting cancer recurrence is highlighted in this cutting-edge literature review. Even though a number of algorithms show promise for bettering patient outcomes in cancer, further research and development—especially in the areas of deep learning and ensemble methods—is necessary. Predictive models must use a variety of datasets and clinical parameters in order to achieve complete accuracy. The advantages that might result from continued research are numerous and include improved survival rates and tailored treatment regimens. This study acts as a beacon, emphasizing the necessity of continued research endeavors, clinical trial validation, and ML development in order to fully achieve the technology's potential to transform cancer care.
Machine Learning, Cancer, Recurrence prediction, Support Vector Machine, Deep Learning, CNN, ANN.