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Detection of Breast Cancer with Python
Abstract
Global cancer data confirms more than 2 million women diagnosed with breast cancer each year reflecting majority of new cancer cases and related deaths, making it significant public health concern. But fortunately, it is also the curable cancer in its early stage. Early diagnosis of breast cancer with timely and effective treatment services improves the prognosis and survival of patients. During classifying tumors, there are significant chances of error and false diagnosis which is needed to be worked upon. Accurate classification can prevent patients from unnecessary treatments. Thus, it is important to accurately classify patients into malignant and benign groups with right diagnosis. This study is based on machine learning (ML) algorithms, aiming to review python technique and its application in breast cancer diagnosis and prognosis by building simple machine learning model. Machine learning has unique advantage as it detects critical features from complex breast cancer datasets. The methodology is widely used for classification of pattern and forecast modelling. The primary data for this study is extracted from Wisconsin breast cancer database (WBCD). It is the benchmark database which compares result via different algorithms.
Keywords
References
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