Abstract
The fierceness of breast cancer is obvious for the patients and the field of medical studies for its malignant destruction in the human body. The failure in diagnosis may put patients in suffering from high financial pressure and may cause the patients’ physical health to deteriorate. The key in this research of contrasting different kinds of algorithms and varied types of classifications is to support physicians to make a better judgment to prevent misdiagnosis. The research introduced many aspects of data on breast cancer detection to make the data inputs of algorithms able to satisfy doctors’ needs. Moreover, the varied kinds of features will specify and confirm the presence of breast cancer tumours instead of treating them as a part of the benign tissues. With the information above, the research found a difference in accuracy between the classification and tried to make a comparison to conclude that most satisfy and support the actual use of these data to physicians, who may see the symptoms with misunderstanding. Thus, Random Forest was qualified to be evaluated as the most applicable algorithm of its excellence in processing higher dimensional, which is dynamic, data.
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