In order to boost the likelihood of successful treatment and long-term survival for cancer patients, it is important to identify and prevent cancer in its earliest stages using a linear regression method. Models that predict cancer risk factors and early symptoms can be created using the linear regression algorithm. These models can be trained using historical datasets of cancer patients' demographics, medical histories, and outcomes of diagnostic tests. Doctors may screen patients and determine which ones are more likely to have cancer or who may already have it but be in the early stages by utilizing these prediction models. This enables early detection and treatment, which can significantly raise the likelihood of positive results. On the basis of each patient's unique risk factors and medical background, these models can also be used to create individualized treatment programs for them. Better treatment outcomes for patients may arise from more focused and effective care. In general, early detection and treatment of cancer using the linear regression algorithm has the potential to save lives, enhance patient outcomes, and lessen the total toll that cancer has on people and society. Overall, a comprehensive and rigorous procedure of data collection, preprocessing, feature selection, model training, evaluation, deployment, monitoring, and updating is required for the methodology of employing the linear regression algorithm to identify and prevent cancer in its early stages and it will found 98.2 % accuracy in the model.

Cancer, Cancer detection, Cancer Prevention, Breast Cancer, Linear Regression Algorithm, Cancer Prediction, Accuracy.