Abstract—Lung cancer surgery is risky such that sometime
patients died after surgery. To reduce loss, we try to create a
computational model to anticipate in advance the
post-operative survival among the lung cancer patients using
statistical and machine learning algorithms. The dataset used in
our model building process is data of patients who underwent
lung cancer surgery comprising of 470 records with 17
attributes. These data were collected at Wroclaw Thoracic
Surgery Centre, Poland during the years 2007 to 2011. For the
purpose of validating the built model, we partitioned this
dataset into training set and test set with the ratio 70% : 30%
and random it 10 times to obtain 10 pairs of training-test set.
The training dataset is used as input to build prediction models
for the post-operative survival in the lung cancer patients by
applying logistic regression and support vector machine (SVM)
algorithms. The obtained two models are then compared to
choose the best one with the highest predictive performance
based on the mean accuracy of the ten iterations. As a result of
comparison using test dataset, prediction model built from the
logistic regression reaches 82.38% on its average accuracy,
while the SVM approach yields 75.67% of its average accuracy.
Index Terms—Post-operative survival assessment, lung
cancer, machine learning, logistic regression, support vector
machine.
K. Sriwong, K. Kerdprasop, N. Kerdprasop are with the School of
Computer Engineering, Suranaree University of Technology, Thailand
(e-mail: nittaya@sut.ac.th).
P. Chuaybamroong is with the Department of Environmental Science,
Thammasat University, Rangsit Campus, Thailand.
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Cite: Kittipat Sriwong, Kittisak Kerdprasop, Paradee Chuaybamroong, and Nittaya Kerdprasop, "A Computational Method to Assess Post-operative Risk of
Lung Cancer Patients," International Journal of Modeling and Optimization vol. 10, no. 5, pp. 145-149, 2020.
