A Survey on the Data Mining Techniques Used for Breast Cancer Predictions

Abstract

Breast cancer has become a major cause of death for women in the world. The most effective way to reduce the rate of death caused by breast cancer is early detection. In the last few years, there has been an increase in the usage of data mining techniques on medical data, to discover useful patterns or trends that are used in analysis, diagnosis and decision making. Data mining algorithms, when used appropriately, are efficient in improving the quality of prediction, diagnosis and disease classification. In this paper we present an overview of the data mining techniques used for the classification of medical data and also highlight some related works in breast cancer predictions, using a table to compare the results obtained from the classifications.

Breast Cancer Analysis Using Data Mining Techniques

“…Ishola and Ajibola have discussed some research works done for diagnosis or predicting breast cancers, and some of the data mining algorithms and techniques being used, with different datasets [5]. Association Rules, Classification, Prediction, Clustering, Decisions Tress, and Neural Networks.…”