When it comes to significant neoplastic illnesses that affect women, breast cancer is by far the most common cause of death and is therefore often regarded as the deadliest of all malignancies. This is due to the absence of several early warning signs and symptoms, as well as the illness's aggressive aggressiveness. A molecular docking technique was also used to investigate the binding connection between proteins and curcumin. The Swiss model was used to make the proteins, and the PyMol programme was used to study them. Expasy's ProtParam Proteomics service was used to explore the impact of the proteins' physicochemical properties. In addition, the SOPMA Self Optimized Prediction Method from Alignment server was used to investigate the proteins' secondary structures. Following that, each of them was subjected to an analysis using PROCHECK, ProQ, ERRAT, and Verify3D. During the first phase, the BSP-Slim server was used to dock the proteins made by breast cancer cell lines with curcumin. In this study, SVM, ANN, and Decision tree algorithms in breast cancer will be investigated, and this approach may be used to all types of malignancies.

Data Mining,Classification Algorithms,Decision Tree,SOPMA,affected Images