Abstract—Survivability of patients suffering from breast cancer varies according to the stages. The early detection of breast cancer increase the longevity of patients. However, the number of risk factors involved in the detection exponentially increases with the medical examinations. The need for automated data mining techniques to enable cost-effective and early prediction of cancer is rapidly becoming a trend in healthcare industry. The optimal techniques for prediction and diagnosis differs significantly due to the risk factors. This study reviews article provides a holistic view of the types of data mining techniques used in prediction of breast cancer. On a whole, the computer-aided automatic data mining techniques that are commonly employed in diagnosis and prognosis of chronic diseases include Decision Tree, Naïve Bayes, Association rule, Multilayer Perceptron (MLP), Random Forest, and Support Vector Machines (SVM), among others. The accuracy and overall performance of the classifiers differ for every dataset and thereby this article attempts to provide a mean to understand the approaches involved in the early prediction.
Index Terms—Breast cancer, data mining, early prediction.
The authors are with the Asia Pacific University of Technology & Innovation, Kuala Lumpur 57000, Malaysia (e-mail: email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
Cite: Keerthana Rajendran, Manoj Jayabalan, Vinesh Thiruchelvam, and V. Sivakumar, "Feasibility Study on Data Mining Techniques in Diagnosis of Breast Cancer," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 328-333, 2019.