International Journal of Research in Arts and Science
ISSN: 2394-9759 | Frequency: 4 Issues/Year
Impact Factor: 0.387 | International Scientific Indexing(ISI) calculate based on International Citation Report(ICR)
Breast Cancer Tumor Categorization using Logistic Regression, Decision Tree and Random Forest Classification Techniques
Abstract:
The abnormality in the cells may lead to cancer. There are around 200 categories of cancer, wherein after skin cancer, breast cancer is the most common cancer diagnosed in women. Both men and women could be affected with breast cancer, but the amount of women getting affected is more than men. Even though the death rate of breast cancer people is declining, the early identification will reduce more cell abnormality. The two types of breast tumors benign and malignant could be classified using the data mining classification techniques. The logistic regression, decision tree and random forest are the techniques are discussed in this chapter and experimental results with metrics sensitivity, specificity, positive predictive, negative predictive and accuracy have been analyzed to identify the best suitable classification technique for the data set are considered.
Keywords: Logistic Regression, Decision Tree, Random Forest, Malignant, Benign.
Volume: 5 | Issue: Holistic Research Perspectives [Volume 4]
Pages: 282-289
Issue Date: August , 2019
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