Stochastic Self-Organizing Map and Proposed Enlarge C4.5 to Diagnose Breast Cancer

Timely and accurate Breast Cancer (BC) prediction allows healthcare providers and doctors to take suitable decisions to treat the patients. Thus, this study employed a strategy based on Deep Learning (DL) to diagnose BC. The study intends to cluster the BC data by the proposed Stochastic Self-Organizing Map (SOM) as it has the ability to process complex data. On the other hand, Enlarge C4.5 (E-C4.5) algorithm is introduced to predict the BC cases based on the clustered outcomes. The BC dataset is loaded and pre-processing is performed where dimensionality reduction is executed to select only the relevant features for clustering. This process eases the clustering. Then, clustering is undertaken by the proposed Stochastic SOM. Here, all the identical data are grouped as clusters which makes it easy for prediction. Followed by this, BC is predicted by the proposed E-C4.5 algorithm. The analytical outcomes explore that the proposed methods are effective than the conventional methods as the proposed method shows a high accuracy rate, precision rate, recall rate and F1-score rate. The misinterpretation rate is also found to be minimum on implementing the proposed method, which is confirmed through the confusion matrix. The proposed method also determines the malignant and benign counts.