Using MLP and SVM for predicting survival rate of oral cancer patients
In this paper, we have attempted to build multilayer perceptron (MLP) and support vector machine (SVM) models for predicting survivability of the oral cancer patients who visit the ENT OPD. MLP and SVM have been applied in the past by few researchers for prediction of oral cancer using the genetic database. However, the database used for current research has the attributes like clinical symptoms, history of addiction, diagnosis, investigations, treatments and follow-up details which is gathered from presentations and review graphs related to oral malignancy from ENT and head and neck department. The MLP and SVM models are compared on the basis of various estimation criteria to identify the most effective model. Experimental result shows that accuracy of classification of SVM model is 73.56%, whereas MLP model is 70.05%; specificity of SVM model is 73.53%, whereas MLP model is 65.36%; and sensitivity of MLP model is 77.00%, whereas SVM model is 73.56%. SVM displays better results in terms of true negative, false negative, geometric mean of sensitivity and specificity, positive predictive value, geometric mean of positive predictive value and negative predictive value, precision, F-measure, area under receiver operating characteristics curve and lift and gain chart. Hence, it may be concluded that SVM is a most favourable model for predicting survival rate of oral cancer patients.
oral cancer multilayer perceptron support vector machine classification data mining early detection