A New Expert System for Diagnosis of Lung Cancer: GDA-LS_SVM

Abstract

In nowadays, there are many various diseases, whose diagnosis is very hardly. Lung cancer is one of this type diseases. It begins in the lungs and spreads to other organs of human body. In this paper, an expert diagnostic system based on General Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM) Classifier for diagnosis of lung cancer. This expert diagnosis system is called as GDA-LS-SVM in rest of this paper. The GDA-LS-SVM expert diagnosis system has two stages. These are 1. Feature extraction and feature reduction stage and 2. Classification stage. In feature extraction and feature reduction stage, lung cancer dataset is obtained and dimension of this lung cancer dataset, which has 57 features, is reduced to eight features using Generalized Discriminant Analysis (GDA) method. Then, in classification stage, these reduced features are given to Least Squares Support Vector Machine (LS-SVM) classifier. The lung cancer dataset used in this study was taken from the UCI machine learning database. The classification accuracy of this GDA-LS-SVM expert system was obtained about 96.875% from results of these experimental studies.