A Parellel two Stage Classifier for Breast Cancer Prediction and Comparison with Various Ensemble Techniques

Ali Tariq Nagi, Ahmad Wali, Adnan Shahzada, Muhammad Masroor Ahmad

Abstract


Life is a blessing but some diseases snatch human life away before even they are being diagnosed. One such horrifying disease is cancer. Among cancer, the most leading and common type is breast cancer.  The actual problem lies in the fact that it is very hard and time consuming for even the most experienced medical specialist to detect the disease with high accuracy but the machines and modern computer science techniques have increased the accuracy and reduced the amount of time taken to diagnose cancer. In the subject paper, a new parallel machine learning technique called the two-stage classifier for identifying breast cancer is presented and compared with various existing techniques in terms of accuracy and percentage error reduction. The proposed technique turns out to be better not only in terms of parallelism but also in terms of the evaluated metrics and reduced the error percentage to almost 50% in one of the cases.

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DOI: http://dx.doi.org/10.21015/vtcs.v15i3.523

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