Feasibility Study on Data Mining Techniques in Diagnosis of Breast Cancer

Keerthana Rajendran, Asia Pacific University of Technology & Innovation, Kuala Lumpur 57000, Malaysia, Manoj Jayabalan, Vinesh Thiruchelvam, V. Sivakumar
2019 International Journal of Machine Learning and Computing  
Feasibility study on data mining techniques in diagnosis of breast cancer http://researchonline.ljmu.ac.uk/id/eprint/11684/ Article LJMU has developed LJMU Research Online for users to access the research output of the University more effectively. 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
more » ... es 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, Naï ve 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.
doi:10.18178/ijmlc.2019.9.3.806 fatcat:emdwvc33jjbt3lyoblcjcspgoq