2Department of Radiation Oncology, University of Health Sciences, Kartal Dr. Lutfi Kırdar Training and Research Hospital, İstanbul-Turkey
3Department of Computer Engineering, Beykent University Faculty of Engineering and Architecture, İstanbul-Turkey
4Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir-Turkey DOI : 10.5505/tjo.2021.2566 OBJECTIVE
The problem in gastric cancer patients is multifactorial and it is not possible to use one factor alone to predict disease survival. Machine learning (ML) algorithms have become popular in the medical field, recently. We aimed to evaluate the power of ML algorithms for predicting deaths due to gastric cancer.
METHODS
We reevaluated the retrospective data published. Seven different ML algorithms (logistic regression [LR],
artificial neural networks/multilayer perceptron, gradient boosted trees, support vector machine, random
forest, naive Bayes, and probabilistic neural network) tried to predict disease-related deaths using
the significant variables effective on disease-specific survival (DSS) obtained from univariate analysis.
RESULTS
Median follow-up time was 34 months (4-156 months), and the death with disease occurred in 194 (86.6%)
patients in the follow-up period. The median DSS was 22 (4-139) months. Using the significant variables
effective on DSS obtained from univariate analysis, the highest accuracy rate (99%) was the best in the LR,
and only one patient was classified incorrectly.
CONCLUSION
We can successfully predict the treatment outcomes such as disease-related deaths in gastric cancer
patients using ML algorithms.