Published Paper Details:

One of the most common cancers globally is breast cancer in women. According to data from reputable health organizations, breast cancer, therefore, causes a serious human setback that raises the death rate. Any health issue that is detected early may have a speedy recovery. In this research study, many factors that characterize the state of human existence now were used to analyze breast cancer. This uses machine learning approaches to anticipate the occurrence of breast cancer by identifying and analyzing human life quality variables. The information used in this evaluation inquiry was acquired from a sample of Bangalore area people as well as information that was factually stored online. Classification methods the measurements analysed to present the results utilising the various error measures RMSE, MAE, and Kappa measuring esteem include accuracy, recall, precision, and F-measures. K-means++ clustering computation and the C4.5 decision tree techniques are used in this investigation. The accuracy of the suggested machine learning model's C4.5 computation was 98.97%, and it�s K-means++ grouping accuracy was 97.98% over the real-time data.