Abstract
Medical professionals need a reliable methodology to predict diseases. The process of Machine Learning is used to identify unknown and useful patterns to assist in important tasks of disease prediction and treatment. The techniques that combine multiple classifiers are used for classifying the data sets. Each feature of data sets in the Wisconsin Breast Cancer Dataset (WBCD) collected from fine needle ambitious from human breast tissue. This data set was used to develop a predictive model for the classification and prediction of breast cancer. Support Vector Machine algorithm exhibited good performance when differentiating to other algorithms in such a way that it could be confirmed as the effective classification algorithm with respect to the accuracy, sensitivity, and mean absolute error when applied to diabetes, data sets. Classification and prediction accuracy varied with the quality of the data set.
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Rao, S.V.A., Rao, P.R.K. (2021). A Predictive Model for Classification of Breast Cancer Data Sets. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-030-88244-0_36
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