Machine learning algorithms predicting bladder cancer associated with diabetes and hypertension: NHANES 2009 to 2018

Abstract

Bladder cancer is 1 of the 10 most common cancers in the world. However, the relationship between diabetes, hypertension and bladder cancer are still controversial, limited study used machine learning models to predict the development of bladder cancer. This study aimed to explore the association between diabetes, hypertension and bladder cancer, and build predictive models of bladder cancer. A total of 1789 patients from the National Health and Nutrition Examination Survey were enrolled in this study. We examined the association between diabetes, hypertension and bladder cancer using multivariate logistic regression model, after adjusting for confounding factors. Four machine learning models, including extreme gradient boosting (XGBoost), Artificial Neural Networks, Random Forest and Support Vector Machine were compared to predict for bladder cancer. Model performance was assessed by examining the area under the subject operating characteristic curve, accuracy, recall, specificity, precision, and F1 score. The mean age of bladder cancer group was older than that of the non-bladder cancer (74.4 years vs 65.6 years, P < .001), and men were more likely to have bladder cancer. Diabetes was associated with increased risk of bladder cancer (odds ratio = 1.24, 95%confidence interval [95%CI]: 1.17–3.02). The XGBoost model was the best algorithm for predicting bladder cancer; an accuracy and kappa value was 0.978 with 95%CI:0.976 to 0.986 and 0.01 with 95%CI:0.01 to 0.52, respectively. The sensitivity was 0.90 (95%CI:0.74–0.97) and the area under the curve was 0.78. These results suggested that diabetes is associated with risk of bladder cancer, and XGBoost model was the best algorithm to predict bladder cancer.