## 5 Discussion  
The findings of this study align with previous research on cancer incidence forecasting in Canada but extend existing methodologies by incorporating machine learning (ML) models to enhance predictive accuracy. Past studies, such as those by [PERSON] et al. [16, 17, 18], have relied heavily on statistical models like CANPROJ, which use age-period-cohort (APC) frameworks to extrapolate trends. While effective in capturing historical linear patterns, such models assume that past trends will continue predictably, limiting their ability to reflect complex, nonlinear dynamics in cancer data. In contrast, the machine learning models used in this study, particularly the hybrid ARIMA-LSTM model, are adaptive and data-driven, capable of learning from fluctuations and underlying patterns that may not follow a strict statistical progression. Figure 6: Forecasted cancer incidence rate per 100,000 people across various age groups in Canada (the yellow-shaded area shows the predicted values). Figure 7: Forecasted cancer incidence rate per 100,000 people in Canada by sex (the yellow-shaded area shows the predicted values). For instance, [PERSON] et al. projected new cancer cases up to 2024 using historical data and demographic adjustments. Our approach not only extends these projections to 2026, offering a more updated outlook, but also improves accuracy by integrating deep learning with statistical time series models. The hybrid ARIMA-LSTM model consistently yielded lower error rates than other models, demonstrating that this combined approach effectively captures both linear and nonlinear trends in cancer incidence forecasting. The analysis reveals distinct patterns and trends across sex, geography, and age groups. Males exhibit a steady increase in both new cancer cases and incidence rates, while females experience a rise in absolute cases but a decline in incidence rates. The observed trend in female cancer incidence--where absolute cancer cases are increasing while incidence rates are declining--suggests demographic shifts, notably population growth and aging, rather than improvements in cancer treatments alone. Additionally, improvements in screening programs and early detection efforts (such as breast and cervical cancer screening) documented in the existing literature likely play a role in stabilizing or reducing incidence rates for certain cancer types. [PERSON] et al. confirmed that despite an overall decline in cancer incidence and mortality rates, Canada is expected to see an increase in new cancer cases and deaths in 2024, primarily due to its growing and aging population. While advancements in prevention, screening, and treatment have mitigated the effects of certain cancers, these near-term projections emphasize the ongoing burden cancer may pose to individuals and the Canadian healthcare system [18]. Geographically, regions like Alberta, British Columbia, Ontario, and Quebec show increasing cancer cases and incidence rates, indicating a growing cancer burden. In contrast, provinces such as Newfoundland and Labrador demonstrate a declining trend, possibly reflecting effective local cancer control measures or public health strategies. Age-wise, cancer incidence remains stable among children but increases significantly among middle-aged and older adults, especially in individuals aged 75 and above. These findings underscore the need for targeted public health interventions explicitly tailored to demographic groups and geographic regions showing an increased cancer burden. The steady rise in cancer cases among older adults and specific provinces highlights the importance of developing age-specific and region-specific cancer prevention and control strategies. Meanwhile, stability in pediatric cancer incidence suggests that existing pediatric cancer strategies may be effective but require sustained support. Given the complexity of cancer incidence trends, selecting an accurate forecasting model is critical for public health planning.