Data Mining Techniques for Early Detection of Breast Cancer

Maria Cruz, Jorge Bernardino

Abstract

Nowadays, millions of people around the world are living with the diagnosis of cancer, so it is very important to investigate some forms of detection and prevention of this disease. In this paper, we will use an ensemble technique with some data mining algorithms applied to a dataset related to the diagnosis of breast cancer using biological markers found in routine blood tests, in order to diagnose this disease. From the results obtained, it can be verified that the model got an AUC of 95% and a precision of 87%. Thus, through this model it is possible to create new screening tools to assist doctors and prevent healthy patients from having to undergo invasive examinations.

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