摘要：Cancer is one of the diagnostic threats appearing to the mankind in this century and among various cancers, breast cancer is the major death causing disease which occurs mainly in women belonging to age between 45 and 60. Early detection and its appropriate treatment can significantly reduce the chances of their death. The objective of this review paper was to study the current systems to develop models with higher classification accuracy for prediction of breast cancer symptoms, their chances of recurrence at the early stage and also their chances of survivability. Here investigation was also done to verify whether comparable accuracy can be achieved even with lesser number of features or not. Initially the feature set is reduced to avoid the over fitting problem and then various machine learning techniques are applied. Here, three different types of feature selection techniques and various machine learning classifiers have been discussed. Further, the comparative analysis among feature selection methods has been done based on their accuracy, computational speed and their dependency on machine learning classifiers. Moreover, the advantages and disadvantages of various classifiers are also discussed. A study of different results from past years have been compared based on the applied classifier, feature selection technique, number of features used and different performance measures like accuracy, sensitivity etc. From different research studies, it is found that comparable accuracy can be achieved even with lesser number of features, which overall reduces the computational complexity of the model. It have discovered that different researchers have found the optimal number of features by hit and trial method which is a very difficult task and to overcome this difficulty, the future scope has been discussed.
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