Enhanced Breast Cancer Prediction Through Block Processing of Gene Data and Random Forest Regression

The uses Random Forest Regression (RFR) in conjunction with block processing of cancer gene data to propose a unique method for the earlier prediction of breast cancer. Our approach starts with gathering and pre-processing large genomic datasets, with a particular emphasis on genes frequently linked to breast cancer. Segmenting the data into smaller, more manageable units improves the accuracy and processing efficiency. To find the most pertinent genetic markers, a thorough feature selection process is applied to each block. These variables are then loaded into a Random Forest Regression model, which was selected due to its capacity to represent intricate gene relationships and its resilience when dealing with high-dimensional data. A large dataset is used to train and validate the RFR model, guaranteeing a fair representation of the various subtypes of breast cancer. When compared to conventional approaches, our results show a significant improvement in the predicted accuracy and early detection skills.