Lung Cancer Prediction and Classification Using Recurrent Neural Network

Abstract

Lung cancer is the leading causes which affect both men and women in all over countries. Lung cancer which gives the low prognosis, and result in a high death rate. This world is computing sector and it is fully automated and now-a-days the medical industry is also fast moving into the digital world so they automating itself with the uses of image recognition and data analytics. This paper endeavors to inspect the prediction and the classification by the uses of three classifiers which is Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Recurrent Neural Network (RNN), this all will classify and give the accuracy value for the lung cancer and this accuracy value is more helpful to find whether the cancer is in early stage are critical stage. So that we can save many patients before the advanced stage. Basically, all the information of patients will be taken from UCI datasets to known how much of patients are affected by lung cancer. The principle of this paper is to the execution of the prediction, classification and accuracy value of the algorithms. The experimental results of prediction and classification shows that SVM gives the result with 78.56%, and KNN with 65.40%. and accuracy value result is from RNN with 92.75%.