Symptom Wise Age Prediction of Cancer Patients Using Classifier Comparison and Feature Selection

Abstract

Cancer has become one of the most life threatening disease over the past few decades. Especially on Bangladesh the number of people being affected by cancer is increasing in an agitating rate. Again cancer, diagnosed after a certain stage, inevitably leads towards death. To abate this vicious upheaval of cancer, awareness has no other alternative. Our research primarily focuses on detection of certain age group, according to the corresponding cancer diagnosis and relevant factors. In order to do so, we have implemented logistic regression, support vector machine and convolutional neural network on the original dataset. Afterwards, two feature selection methods (Feature Importance Ranking Method and Recursive Feature Elimination) have been applied on the dataset to extract out the most significant features. The three classifier comparison has been implied on both the feature selection methods. It is found that the classifier accuracy on the extracted features is significantly better in case of Recursive Feature Elimination rather than Feature Importance Ranking Method.