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IJERTV9IS060639
Comparative Study of Breast Cancer Diagnosis using Data Mining Classification

Yopie Noor Hantoro

Breast Cancer is often suffered by women and it is an enemy to millions of women all over the world. The most important strategy to prevent death is to do early detection of the breast cancer and provide modern treatments. Along with the development of medical technology and information technology, various methods have been developed to detect the presence of breast cancer, one of which is the machine learning classification technique. In this study, performance comparison is conducted on three machine learning algorithm i.e. Multilayer Perception (MLP), Random Forest (RF) and Support Vector Machine (SVM). The dataset is sourced from Wisconsin Breast Cancer Diagnostic (WBCD). The performance comparison is evaluated by measuring accuracy, precision and recall values. Result of this study confirm that, using the k-fold cross validation technique, the MLP algorithm has the highest performance.

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