Diagnosis of lung cancer using artificial immune system

Abstract

In this study, we implement the Artificial Immune System method to increase the number of data in the lung cancer dataset and obtain higher prediction rate for the diagnosis of lung cancer. Artificial Immune System is modified with the weights of features. Dataset dimension is decreased to raise the performance of this algorithm by using Pearson Correlation Coefficients. The system is also compared to other methods like k-Nearest Neighbor and Artificial Neural Networks that are commonly used in previous studies. As a result, the proposed weighted Artificial Immune System has the highest accuracy rate as 82% on normalized dataset and appears to be the second fastest method after k-Nearest Neighbor.