Cancer disease prediction with support vector machine and random forest classification techniques

The Concept of classification and learning will suit well to medical applications, especially those that need complex diagnostic measurements. Therefore classification technique can be used for cancer disease prediction. This approach is very much interesting as it is part of a growing demand towards predictive diagnosis. From the available studies it is evident that classification and learning methods can be used effectively to improve the accuracy of predicting a disease and its recurrence. In the present work classification techniques namely Support Vector Machine [SVM] and Random Forest [RF] are used to learn, classify and compare cancer disease data with varying kernels and kernel parameters. Results with Support Vector Machines and Random Forest are compared for different data sets. The results with different kernels are tuned with proper parameters selection. Results are analyzed with confusion matrix.