An Improved Algorithm To Predict Recurrence Of Breast Cancer

Computer Engineering

In today’s fast growing world people are becoming more and more prone to diseases, whether they live in developed or third world countries. The tremendous advancement in technology has led medical information systems in hospitals and medical institutions become larger and larger and in turn the process of extracting useful information is becoming more and more difficult and time consuming. Breast cancer is the one of the most common cancer in women and thus the early stage detection in breast cancer can provide potential advantage in the treatment of this disease. Early treatment not only helps to cure cancer but also help in its prevention of its recurrence. Datamining algorithm can provide great assistance in prediction of early stage breast cancer that always has been a challenging research problem. The main objective of this research is to find how precisely can these datamining algorithms predict the probability of recurrence of the disease among the patients on the basis of important stated parameters. An approach is proposed for disease prediction that combines Support vector machine and bagging using Ensemble learning. The research highlights the performance of Support vector machine with using bagging method in ensemble learning. Experiments show that Support vector machine is the best predictor in all classification algorithms while combining with bagging .The result indicates that accuracy of Support Vector Machine is 81% without using ensemble learning but by combining with bagging in ensemble learning, the accuracy of Support Vector machine is 84.61% which is the highest accuracy that have predicted.

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