A novel model for accurate and fast prediction of cancer incidence
Abstract Background Predicting cancer incidence has long been a challenge for clinicians and researchers. Accurate predictions are essential for health planning to ensure adequate resources for diagnosis, treatment, and rehabilitation. Current prediction methods rely on historical data, assuming per...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
|
| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12889-025-22624-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Background Predicting cancer incidence has long been a challenge for clinicians and researchers. Accurate predictions are essential for health planning to ensure adequate resources for diagnosis, treatment, and rehabilitation. Current prediction methods rely on historical data, assuming persistent patterns of cancer incidence. Method In this study, the Google Trends tool was used to obtain the relative search volume index (RSVI) for the topic “cancer” each year from 2017 to 2023 in the United States and worldwide. The proposed model incorporated actual cancer incidence rates and yearly changes in RSVI. Results The model was applied to predict the rates of new cancer cases in fifty American states over four consecutive years (2017, 2018, 2019, 2020). The selection of years was restricted with data availability. In most states, the percentage error did not exceed 6%. The high degree of similarity between the actual and predicted cancer incidence rates was notable. Similar results were obtained when predicting cancer incidence rates in the countries studied. Conclusion The model has successfully provided accurate short-term predictions of cancer incidence rates across all 50 American states and 54 countries since 2017. |
|---|---|
| ISSN: | 1471-2458 |