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@article{156469,
        author = {B. Lavanya and I. Dilshad Banu},
        title = {A SYSTEMATIC STUDY ON THE TECHNIQUES USED FOR THE PREDICTION OF BREAST CANCER},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {3},
        pages = {776-784},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156469},
        abstract = {Breast cancer is regarded as the most serious problem among women. This is a severe threat to women's society, similar to climate change, urbanization, food culture, etc. The tools available in data mining provide a significant contribution to the field of medical diagnostics in terms of accurate disease prediction.  The probability of saving a breast cancer patient is majorly dependent on its stage detection and initiation of treatment. To address these challenges, this paper involves an overview of several data mining approaches that are specifically applied to breast cancer prediction. This paper shows the comparison of diverse classification and clustering algorithms. Varied classification algorithms and clustering algorithms are used in this survey paper. We have compared the performances of various machine learning and deep learning techniques such as Support Vector Machine, Decision Trees, Logistic Regression, Naive Bayes,Convolutional Neural Networks (CNN) etc. We have used publicly available breast cancer datasets, for testing several approaches for autonomous tumour classification. However, the proposed Convolutional Neural Networks (CNN) model classifier has the highest classification accuracy, according to experimental results.},
        keywords = {Breast cancer; Accurate prediction; Treatment; Early diagnosis;Convolutional Neural Networks (CNN) },
        month = {},
        }