A NOVEL APPROACH FOR BREAST CANCER PREDICTION USING IMPROVED GATED RECURRENT UNIT

The cells that make up the breast tissue can transform into a tumorous malignancy known as breast cancer. Although males are not immune, it is more frequent in women and is among the most common malignancies globally. In most cases, the illness starts in the breast's ducts or lobules but, if left untreated, can metastasize to other organs. Predicting the likelihood of breast cancer is essential for both early detection and treatment planning. This study presents an advanced approach to breast cancer prediction using an Improved Gated Recurrent Unit (GRU) model. The methodology begins with preprocessing a CSV dataset of numerical records, where Recursive Feature Selection with Extra Tree Classifier (RFET) is employed to identify the most relevant features, enhancing the model's predictive accuracy. Following feature selection, an Improved GRU model is utilized for classification and predictive modelling. The improved GRU architecture incorporates optimizations to improve learning efficiency and accuracy, utilizing temporal dependencies within the data. High predictive performance was achieved by the suggested method, according to the findings, providing a useful tool for early identification and diagnosis of breast cancer.