Abstract
Breast cancer is the major and detrimental ailment amid all of the afflictions. Females are regularly affected through this disease. Data mining is a knowledge innovation progression to detect the sickness among enormous quantity of information. We proposed an approach used for the prognostication of tumor and presented through support vector machine and neural network classification methods. 10-fold and 5-fold cross validations are applied in the intended system to obtain precise results. The breast cancer database is used in this procedure which is from UCI machine learning repository. By using WEKA tool we studied the both classification techniques which are support vector machine and neural network classification models with 5 and 10-fold cross validations. In addition, support vector machine with 5-fold cross validation got high accuracy.
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Navya Sri, M., Sailaja, D., Hari Priyanka, J.S.V.S., Chittineni, S., RamaKrishnaMurthy, M. (2020). Performance Evaluation of SVM and Neural Network Classification Methods for Diagnosis of Breast Cancer. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-24322-7_44
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DOI: https://doi.org/10.1007/978-3-030-24322-7_44
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