Outlier Selection Method for Classification of Breast Cancer

One of the deadliest diseases, cancer is believed to be the second biggest cause of death for women internationally. The patient's life may be saved by early cancer identification. Breast tissues give rise to cancerous tumors that spread to other regions of the body and ultimately cause death. Cancer in women cases are anticipated to rise to a total of 2.3 million or higher in 2023, according the World Health Organization. Cancer may impact both males and females. A prompt and accurate diagnosis contributes to a higher patient survival rate. It is challenging to support medical professionals in developing a regimen that might prolong the life of valetudinarians because of the need for precise prophecy to identify symptoms. Rapid detection must be combined with effective cancer therapy, which frequently calls for the highest level of specialized cancer care. Methods based on machine learning can be applied to increase the diagnosis' speed and accuracy. If the precision is flawless, the model will function with greater efficiency and increase breast cancer diagnosis. Two independent machine learning algorithms were employed to perform outlier analysis on the Wisconsin Diagnostic Breast Cancer Dataset in order to increase the accuracy of breast cancer detection.