Prediction of early‐onset colorectal cancer mortality rates in the United States using machine learning

Abstract

Abstract Introduction The current study, focusing on a significant US (United States) colorectal cancer (CRC) burden, employs machine learning for predicting future rates among young population. Methods CDC WONDER data from 1999 to 2022 was analyzed for CRC‐related mortality in patients younger than 56 years. Temporal trends in age‐adjusted mortality rates (AAMRs) were assessed via Joinpoint software. Future mortality rates were forecasted using an optimal Autoregressive Integrated Moving Average (ARIMA) model. Results From 1999 to 2022, we observed 150,908 deaths with CRC listed as the underlying cause, predominantly in males, with an upward trend in AAMR. The ARIMA model projects an increase in CRC mortality by 2035, estimating an average annual percent change (AAPC) of 1.3% overall, 1% for females, and 1.5% for males. Conclusion Our study findings emphasize the need for more robust preventive measures to reduce future CRC mortality among younger population. These results have significant implications for public health policies, particularly for males under 56, and underscore the importance of early screening and lifestyle modifications.

Keywords

• autoregressive integrated moving average
• cancer prevention
• colorectal cancer
• machine learning
• mortality trends