ArticleCancer reports (Hoboken, N.J.)2023
Lifestyle and occupational risks assessment of bladder cancer using machine learning-based prediction models.
Article in Cancer reports (Hoboken, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Evaluation of inflammatory markers in survival analysis of patients undergoing radical cystectomy using machine learning.World journal of urology · 2025Article
- Objective Assessment of Physical Activity in Breast Cancer Survivors: Associations With Adiposity and Metabolic Parameters of Glucose and Insulin-Insights From NHANES.Cancer reports (Hoboken, N.J.) · 2025Article
- Point-Based Prediction Model for Bladder Cancer Risk in Diabetes: A Random Survival Forest-Guided Approach.Journal of clinical medicine · 2024Article
- Lifestyle and occupational risks assessment of bladder cancer using machine learning-based prediction models.Cancer reports (Hoboken, N.J.) · 2023Article
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Authors and funding
7 authors.
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Abstract
backgroundBladder 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.
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