Affiliation:
1. Urology and Nephrology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran
2. British Heart Foundation Cardiovascular Research Centre University of Glasgow Glasgow UK
3. Students' Scientific Research Center, School of Medicine Tehran University of Medical Sciences Tehran Iran
4. Department of Biostatistics, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran
Abstract
AbstractBackgroundBladder cancer, one of the most prevalent cancers globally, can be regarded as considerable morbidity and mortality for patients. The bladder is an organ that comes in constant exposure to the environment and other risk factors such as inflammation.AimsIn the current study, we used machine learning (ML) methods and developed risk prediction models for bladder cancer.MethodsThis population‐based case–control study is focused on 692 cases of bladder cancer and 692 healthy people. The ML, including Neural Network (NN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Gradient Boosting (GB), and Logistic Regression (LR), were applied, and the model performance was evaluated.ResultsThe RF (AUC = .86, precision = 79%) had the best performance, and the RT (AUC = .78, precision = 73%) was in the next rank. Based on variable importance analysis in RF, recurrent infection, bladder stone history, neurogenic bladder, smoking and opium use, chronic renal failure, spinal cord paralysis, analgesic, family history of bladder cancer, diabetic mellitus, low dietary intake of fruit and vegetable, high dietary intake of ham, sausage, can and pickles were respectively the most important factors, which effect on the probability of bladder cancer.ConclusionMachine learning approaches can predict the probability of bladder cancer according to medical history, occupational risk factors, and dietary and demographical characteristics.