Breast Cancer Survival Prediction Using Gene Sequence Data-Machine Learning Methods

Globally, breast cancer is the most prevalent and complex condition affecting people, particularly women. Because of the variability of the disease, prognosis prediction remains a difficult challenge in patients with breast cancer (BRCA), even with increasing early diagnosis and effective therapy. Precise survival forecasting for BRCA patients is crucial for customized care. The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of BRCA International Consortium (METABRIC) databases were used to compile the gene expression and clinical information of BRCA patients, which are examined in this conference. 1094 samples of BRCA-affected patients make up the dataset. Utilizing this information, researchers can examine the gene expression patterns of BRCA patients, potentially leading to the development of prognostic and survival rate prediction techniques. In order to create a prognostic model from this data and forecast survival for BRCA patients, this work employs a machine learning method. The work has reached a 0.999 percent accuracy rate in all situations, which ultimately directs the choice of treatment.