A Comparative Evaluation and Analysis of Machine Learning Approaches for Predicting Breast Cancer

The use of machine learning in numerous domains is the main topic of this essay. It seeks to give a thorough review of the factors supporting its relevance and importance. This manual’s goal is to describe and emphasize the methods that are currently being used or considered both worldwide and in office settings. To forecast illness prognosis, many methods, tools, training, and education have been used, with varying degrees of success. Using these techniques is essential and acceptable in the field of computational biology. They have specifically been used to forecast breast cancer, whether through the evaluation of data on cancer based on profiles or sequences. Prediction of the enzyme class for unidentified breast cancer patients has been one of the important research fields throughout this time. Within the research community, this specific element has attracted a lot of interest and discussion. We have used machine learning methods and classifiers to address this problem. In the area of studying and forecasting enzyme classes, these techniques have shown to be successful. The primary objective of my research is to develop a model for automated analysis using two different algorithms. The intention behind this approach is to create two distinct models that can be tested for their validity and effectiveness. By doing so, we aim to determine which model is most suitable for protein analysis based on its precision and accuracy. This comparative evaluation will help us identify the algorithm that performs best in terms of accurately analyzing protein data.