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Abstract
Purpose
Recurrence is the main factor for poor prognosis of bladder cancer. Therefore, it is necessary to develop new biomarkers to predict the prognosis of bladder cancer. In this study, we used machine learning (ML) methods based on a variety of clinical variables to screen prognostic biomarkers of bladder cancer.
Patients and Methods
A total of 345 bladder cancer patients were participated in this retrospective study and randomly divided into training and testing group. We used five supervised clustering ML algorithms: decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) to obtained prediction information through 34 clinical parameters.
Results
By comparing five ML algorithms, we found that total bilirubin (TBIL) and CA50 had the best performance in predicting the recurrence of bladder cancer. In addition, the combined predictive performance of the two is superior to the performance of any single indicator prediction.
Conclusion
ML technology can evaluate the recurrence of bladder cancer. This study shows that the combination of TBIL and CA50 can improve the prognosis prediction of bladder cancer recurrence, which can help clinicians make decisions and develop personalized treatment strategies.
Ethics Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Affiliated Hospital of Nantong University. Individual consent was waived because of the retrospective nature of our study. Patients’ data were anonymized and maintained with confidentiality.
Disclosure
All of the authors declare that they have no conflicts of interest for this work.