Artificial Neural Networks as a Way to Predict Future Kidney Cancer Incidence in the United States Article

Full Text via DOI: 10.1016/j.clgc.2020.10.008 Web of Science: 000657393800005
International Collaboration

Cited authors

  • Santoni M, Piva F, Porta C, Bracarda S, Heng DY, Matrana MR, Grande E, Mollica V, Aurilio G, Rizzo M, Giulietti M, Montironi R, Massari F

Abstract

  • Renal-cell carcinoma (RCC) incidence is increasing. Our aim was to implement an artificial neural network in order to predict the new cases of RCC in the population starting from population rate, obesity, smoking incidence, uncontrolled hypertension, and life expectancy data in the United States. Preventing risk factors, and in particular hypertension, could help greatly reduce the incidence of RCC. Introduction: The incidence of kidney cancer is increasing; it could be counteracted with new ways to predict and detect it. We aimed to implement an artificial neural network in order to predict new cases of renal-cell carcinoma (RCC) in the population using population rate, obesity, smoking incidence, uncontrolled hypertension, and life expectancy data in the United States. Patients and Methods: Statistics were collected on US population numbers, life expectancy, obesity, smoking, and hypertension. We used MATLAB R2018 (MathWorks) software to implement an artificial neural network. Data were repeatedly and randomly divided into training (70%) and validation (30%) subsets. Results: The number of new RCC cases will grow from 44,400 (2020) to 55,400 (2050), an increase of +24.7%. Our data show that preventing hypertension would have the greatest impact on reduction of the incidence, estimated at -775 and-575 cases per year in 2020 and in 2030, respectively. The prevention of obesity and smoking would have a more limited impact, estimated at-64 and-180 cases per year in 2020 and in 2030, respectively, for obesity, and -173 and-21 cases per year in 2020 and in 2030, respectively, for smoking. Conclusions: Our predictions underline the need for accurate studies on RCC-related risk factors to reduce the incidence.

Authors

Publication date

  • 2021

Published in

International Standard Serial Number (ISSN)

  • 1558-7673

Number of pages

  • 8

Start page

  • E84

End page

  • E91

Volume

  • 19

Issue

  • 2