Abstract

Lung cancers are cancers that begin in the lungs. Other types of cancers may spread to the lungs from other organs. However, these are not lung cancers because they did not start in the lungs. It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of lung cancer, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on lung cancer using principles component analysis (PCA), fuzzy weighting pre-processing and artificial immune recognition system (AIRS). The approach system has three stages. First, dimension of lung cancer dataset that has 57 features is reduced to four features using principles component analysis. Second, a new weighting scheme based on fuzzy weighting pre-processing was utilized as a pre-processing step before the main classifier. Third, artificial immune recognition system was our used classifier. We took the lung cancer dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem.

Keywords: Principles component analysis; Artificial immune system; AIRS; Fuzzy weighting pre-processing; Lung cancer; Medical diagnosis

Article Outline

1. Introduction

2. Natural and artificial immune systems

3. The proposed system

3.1. Overview

3.1.1. Principles component analysis (PCA)

3.1.2. Fuzzy weighting pre-processing

3.1.3. The parameters in AIRS classifier

3.1.4. AIRS classification algorithm

4. The experimental results

4.1. The used lung cancer dataset

4.2. Performance evaluation methods

4.2.1. Classification accuracy

4.2.2. K-fold cross-validation

4.3. Results and discussion

5. Conclusion

Acknowledgements

References