Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine

Fita Sheila Gomiasti; Warto Warto; Etika Kartikadarma; Jutono Gondohanindijo; De Rosal Ignatius Moses Setiadi

doi:10.62411/jcta.10106

Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine

Fita Sheila Gomiasti, Warto Warto, Etika Kartikadarma, Jutono Gondohanindijo, De Rosal Ignatius Moses Setiadi

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

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DOI: https://doi.org/10.62411/jcta.10106

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