Publication Type : Conference Paper
Publisher : International Symposium on Intelligent Systems Technologies and Applications
Source : International Symposium on Intelligent Systems Technologies and Applications (ISTA'17), Manipal University, Karnataka, 2017.
Campus : Bengaluru
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2017
Abstract : Breast cancer is one of the major type of cancer which is the leading cause of death in women. The research work is carried out on the real data of patient records obtained from HealthCare Global Enterprises Ltd (HCG) hospitals. The work analyzes the four major class variables in the dataset, namely death, progression, recurrence and metastasis. The influence of the same 11 predictor variables is explored for each of the class. Various machine algorithms namely Support Vector Machine, Decision Tree, Multi-layer Perceptron and Naive Bayes have been explored for classification of the patient data into various classes. The imbalance in the data is handled using an over sampling technique. The contribution of various attributes in classifying the instances into different classes is also being explored. The model helps in predicting various factors and thus helps in early diagnosis in the breast cancer.
Cite this Research Publication : S. Suresh Shastri, Priyanka Vivek, Dr. Deepa Gupta, Nayar, R. C., Rao, R., and Ram, A., “Breast Cancer Diagnosis and Prognosis using Machine Learning Techniques”, in International Symposium on Intelligent Systems Technologies and Applications (ISTA'17), Manipal University, Karnataka, 2017.