Application of Data Mining Techniques for Breast Cancer Prognosis

Application of Data Mining Techniques for Breast Cancer Prognosis

M. Mehdi Owrang O.
Copyright: © 2015 |Pages: 11
DOI: 10.4018/978-1-4666-5888-2.ch158
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Breast Cancer Prognosis

Although scientists do not know the exact cause of most breast cancers, they do know some of the risk factors that increase the likelihood of a woman developing breast cancer. These factors include such attributes as age, genetic risk, and family history among others.

Key Terms in this Chapter

Association Rule: Is a method of discovering interesting relations between data in the form of IF/THEN statement.

Prognostic Factor: Is a measurable variable that is used in the prediction of survival of breast cancer.

Survivability Rate: Is the probability of surviving and recovering from a disease.

Data Mining: Is the process of analyzing large amount of data in order to discover hidden patterns and relationships.

Prognosis: Is the prediction of the survivability from a disease.

Breast Cancer: Is a malignant (cancerous) growth that begins in the tissues of the breast in an uncontrolled way.

Naïve Bayes: Is a simple probabilistic classifier that is being used for learning to classify data.

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