Applying Machine Learning Techniques To Predict Breast Cancer

Abstract

Breast cancer is most occurring leading cancer amongst women in the world after lung cancer. The doctors are facing problems to prepare a medication treatment owing to a deficiency of the greatest diagnosis results that may prolong patient survival time. Breast cancer was scary and risky before the 20th century. A machine learning algorithm plays a substantial role in early-stage breast cancer prediction. The main objective of a research paper is to find the best classification algorithm. In this paper, the Wisconsin dataset has used. First, it predicts on data set of breast cancer and finds whether it is Malignant(M) or Benign(B). Second, it analyses theperformance and accuracy of various Machine learning algorithms, and third, it compares various machine learning algorithms. The research paper proposed an approach that improves accuracy and enhances the performance of algorithms. The efficient Machine learning algorithms are used such as Naïve Bayes, J48, Sequential Minimal Optimization (SMO) and Instance- Based for K-Nearest neighbor (IBK). The investigational results illustration in the proposed IBK algorithm gives the maximum accuracy of 100% when differentiate from the other three algorithms, this result will benefit in selecting the best classification algorithm for the breast cancer prediction and can be used for detection and treatment