Lung Cancer Classification using Support Vector Machine and Hybrid Particle Swarm Optimization-Genetic Algorithm

• Published in: 2021 International Conference on Decision Aid Sciences and Application (DASA) pp. 751 - 755
• Main Authors: Maulidina, Faisa, Rustam, Zuherman, Pandelaki, Jacub
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.12.2021
• Subjects: classification; genetic algorithm; Lung cancer; Machine learning algorithms; Metaheuristics; optimization; Optimization methods; particle swarm optimization; support vector machine; Support vector machine classification; Support vector machines; Training
• DOI: 10.1109/DASA53625.2021.9682259

Cancer is an uncontrolled growth of abnormal cells in the body. It affects different parts of the body and the ones associated with the lungs is known as lung cancer. Some of the factors increasing a person's risk of the disease include smoking, family history of lung cancer, radiation exposure, and HIV infection. Although the diagnosis of this disease has been made in many ways, there are still some errors in diagnosing the disease. Therefore, this study proposed the classification of lung cancer using the machine learning method to avoid these errors. This involved using the CT Scan dataset obtained from Cipto Mangunkusumo Hospital, Jakarta, Indonesia, and the application of the Particle Swarm Optimization-Genetic Algorithm-Support Vector Machine (PSO-GA-SVM) method of classification. The Particle Swarm Optimization-Genetic Algorithm (PSO-GA) method was used to optimize the parameters of the Support Vector Machine. Moreover, the values of accuracy, precision, recall, and fl-score of the method were measured to evaluate its performance and later compared with the SVM without parameter optimization. The results showed that the classification using PSO-GA-SVM had better performance compared to Support Vector Machine without parameter optimization. This is indicated by the values of the accuracy, precision, recall, and f1-score for the PSO-GA-SVM which were found to be 97.69%, 98.46%, 98.82 %, and 97.66% respectively.