Design on Early Warning System for Renal Cancer Recurrence Based on CNN-Based Internet of Things

Abstract

Kidney cancer is a type of urinary system tumor. The incidence of kidney cancer, which is second only to bladder cancer, has shown an overall upward trend in recent years. However, the early judgment of kidney cancer is still in the imaging biomarker discovery stage. Early detection and treatment cannot be achieved. Based on the natural advantages of the Internet of Things in the medical field, we focused on an intelligent early warning model of renal cancer recurrence and built a renal cancer early warning system integrated with the Internet of Things. We integrated the influencing factors of renal cell carcinoma, constructed a sample set, conducted data analysis and optimized the dataset. Aiming at the instability of renal cancer recurrence, five supervised learning prediction algorithms, including multiple linear regression, Bayesian ridge regression, gradient boosting tree, support vector regression, and convolutional neural network were used to develop a renal cancer recurrence prediction model. The predictive performance of these five algorithms were compared and discussed. Finally, the best renal cancer recurrence prediction model was established by combining a convolutional neural network with an Internet of Things medical framework. This design provided an intelligent early warning system to predict the recurrence time of renal cancer patients. In addition, the warning prompts provided in accordance with the model results can assist doctors in making preliminary judgments of the patient’s condition and has a certain auxiliary effect on the clinical diagnosis and treatment of cancer and kidney cancers.