Classification of breast cancer data using enhanced supervised ML
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Last few years, one of the hot topic in health care informatics is breast cancer, because it is the second main cause of cancer-related deaths in women. Breast cancer can be identified using a biopsy where tissue is removed and studied under microscope. The diagnosis is based on the qualification of the histopathologic, who will look for abnormal cells. However, if the histopathologic is not well-trained, this may lead to wrong diagnosis. With the recent advances in analysis of medical related data and machine learning, there is an interest in attempting to develop a reliable pattern recogniti...
Last few years, one of the hot topic in health care informatics is breast cancer, because it is the second main cause of cancer-related deaths in women. Breast cancer can be identified using a biopsy where tissue is removed and studied under microscope. The diagnosis is based on the qualification of the histopathologic, who will look for abnormal cells. However, if the histopathologic is not well-trained, this may lead to wrong diagnosis. With the recent advances in analysis of medical related data and machine learning, there is an interest in attempting to develop a reliable pattern recognition based systems to improve the quality of diagnosis. In this paper we focus on classification of breast cancer as binary labelled data because is it a Benin or maligned. Thus, the goal is to classify whether the breast cancer is benign or malignant and predict the recurrence and non-recurrence of malignant cases after a certain period. To achieve this we have used machine learning classification methods to fit a function that can predict the discrete class of new input.
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