A comparative analysis of breast cancer detection and diagnosis using data visualization and machine learning applications
Özet
In the developing world, cancer death is one of the major problems for humankind.
Even though there are many ways to prevent it before happening, some cancer types still do not have
any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the
most important thing in its treatment. Accurate diagnosis is one of the most important processes
in breast cancer treatment. In the literature, there are many studies about predicting the type of
breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg
of the University of Wisconsin Hospital were used for making predictions on breast tumor types.
Data visualization and machine learning techniques including logistic regression, k-nearest neighbors,
support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to
this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques
and visualization. The paper aimed to make a comparative analysis using data visualization and
machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of
applications were comparable for detecting breast cancers. Data visualization and machine learning
techniques can provide significant benefits and impact cancer detection in the decision-making
process. In this paper, different machine learning and data mining techniques for the detection of
breast cancer were proposed. Results obtained with the logistic regression model with all features
included showed the highest classification accuracy (98.1%), and the proposed approach revealed
the enhancement in accuracy performances. These results indicated the potential to open new
opportunities in the detection of breast cancer.