A hybrid SVML method for survival of patient post breast cancer operation prediction by using SVM and logistic regression - Agarwal, Rakshanda; Regunathan, Rajeshkannan (International Journal of Engineering and Technology, 2018)
A Support Vector Machine is a supervised linear maximum margin classifier and used in many classification applications. While on the other hand Logistic Regression is a regression model which has a categorical dependent variable. Breast cancer operation is a critical one and the survival of the patient is not sure. For a person to be operated, we must know her survival chances after the cancer operation has been performed. Here, in this paper, we propose a hybrid model of a support vector Machine with Logistic Regression namely, SVML (Support Vector Machine-Logistic) which will help us predict the survival chance of the patient post operation. With this model, we can improve the performance of the SVM classifier in terms of its accuracy. Using our model and dataset, we have increased the accuracy to 85.24% for which SVM gave an accuracy of 78.03% and Logistic Regression gave an accuracy of 72.40%.Â Â