Machine learning–based projections of cancer and depression mortality in the United States through 2035.

e22577 Background: Clinical depression is a relatively common yet frequently overlooked source of suffering among patients with cancer. Recent research has highlighted a strong association between cancer and depression, where patients often develop depressive symptoms due to profound emotional stress, fear of the future, and the psychological burden of the disease . It is crucial to understand this association to establish integrated therapeutic strategies and for patient well-being, as depression has a substantial influence on treatment compliance, quality of life and survival rates. Furthermore, identifying sociodemographic disparities is vital to addressing health inequities and targeting high-risk populations. Methods: We analyzed U.S. national mortality data from 1999–2024 using the CDC WONDER database, identifying cancer as the underlying cause of death with depression as an associated cause . Data were stratified by sex, race, census region, and urbanization to identify specific population-level trends . Age-adjusted mortality rates (AAMR) were log-transformed and modeled using ARIMA-based machine learning forecasting . Machine learning–driven 10-fold time-series cross-validation was implemented to optimize model performance and generalizability, with residual autocorrelation assessed using the Ljung–Box test . Forecasted trends through 2035 were quantified using annual percentage change (APC) and average annual percent change (AAPC). Results: Between 1999 and 2024, there were 40,000 deaths with a marked rise of 10,000 such deaths occurring during the 2021–2024 period . Overall AAMR remained steady initially, followed by increasing mortality trends in females (APC: 5.06%) from 2016 to 2024, contributing to an overall significant rise (AAPC: +1.64%; P < 0.001) while males showed no net significant change (AAPC: +1.03; P=0.12) . However, forecast modeling projected a continued increase through 2035, with an estimated rise of 16.3% among females and a decline of 12.3% among males . Regional and racial disparities were significant, particularly for Black African/American, who showed a rise between 2017 and 2024 (APC: 5.51%) and a projected change of 147.4% by 2035 . Urban-rural differences also persisted, with medium metropolitan areas experiencing a sharp increase from 2015 to 2020 (APC: +8.71%) while the burden in rural areas rose from 2011 to 2020 (APC: +3.88%), followed by a projected change of 19.4% by 2035 . Regionally, the South had an increase from 0.63% APC (1999–2016) to 21.3% through 2035 . Conclusions: Cancer mortality among patients with comorbid depression varies significantly by populations. Projections through 2035 predict a rising burden, particularly among females, Black African/American and rural and south areas. These findings highlight the urgent need for targeted, integrated interventions that prioritize these high-risk groups to mitigate future mortality .