Emperical Evaluation of Machine Learning algorithms for Breast Cancer Data Classification - Kumaravel, S; Vithya, S. Ophilia Domanica (International Journal of Computer Sciences and Engineering, 2025)
Breast cancer (BC) is a deathly cancer disease which occurs mainly in women and the greater number of breast cancer patients leads to death according to global statistics. The early examination of Breast Cancer can augment the durability of patients, and it helps to improve the prompt medication to the patients. Machine learning plays an important role in health care and they are more powerful in classification and prediction process. There are various classification algorithms used based upon then data set. This work is the implementation of few classification algorithms such as Random Forest, K Nearest Neighbor, Navie Bayes, Support Vector Machine, and Artificial Neural Network for breast cancer data set. This paper is the comparative study of these algorithms using R tool. The goal of this paper is to analyze the accuracy of these algorithms. The implementation procedure reveal that the performance of any algorithm varies based on the data set attributes and characteristics