Benchmarking Various Machine Learning Models to Detect Lung Cancer

Iis Afrianty, Liza Afriyanti

Abstract


This study benchmarked and evaluated the performance of various machine learning techniques to detect lung cancer using public datasets. The techniques used include Random Forest, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, C4.5, Bayesian Network, Reptree, Naive Bayes, and P.A.R.T. Evaluation was carried out using metrics such as Accuracy, F-measure, Precision, TPR, ROC, FPR, PRC, and MCC. The results showed that the Support Vector Machine algorithm performed best on balanced dataset distribution, while Random Forest showed stable performance on unbalanced datasets. This study confirms the importance of selecting appropriate algorithms and data distribution to improve lung cancer detection.

Keywords


Benchmarking; Lung Cancer; Machine Learning

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References


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DOI: http://dx.doi.org/10.24014/coreit.v11i2.38590

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