📅 25 March 2024
DOI: 10.62411/jcta.10106
Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 Abstract
This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal with non-linear problems. At the same time, hyperparameter tuning is done through Random Grid Search to find the best combination of parameters. Where the best parameter settings are C = 10, Gamma = 10, Probability = True. Test results show that the tuned SVM improves accuracy, precision, specificity, and F1 score significantly. However, there was a slight decrease in recall, namely 0.02. Even though recall is one of the most important measuring tools in disease classification, especially in imbalanced datasets, specificity also plays a vital role in avoiding misidentifying negative cases. Without hyperparameter tuning, the specificity results are so poor that considering both becomes very important. Overall, the best performance obtained by the proposed method is 0.99 for accuracy, 1.00 for precision, 0.98 for recall, 0.99 for f1-score, and 1.00 for specificity. This research confirms the potential of tuned SVMs in addressing complex data classification challenges and offers important insights for medical diagnostic applications.
🔖 Keywords
#Hyperparameter Tuning; Lung cancer classification; Radial Basis Function Kernel; Random Grid Search; Support Vector Machine
ℹ️ Informasi Publikasi
Tanggal Publikasi
25 March 2024
Volume / Nomor / Tahun
Volume 1, Nomor 4, Tahun 2024
📝 HOW TO CITE
Gomiasti, Fita Sheila; Warto, Warto; Kartikadarma, Etika; Gondohanindijo, Jutono; Setiadi, De Rosal Ignatius Moses, "Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine," Journal of Computing Theories and Applications, vol. 1, no. 4, Mar. 2024.
ACM
ACS
APA
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
0
Sitasi
Cek Google Scholar