Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models
Abstract The World Health Organization has highlighted that cancer was the second-highest cause of death in 2019. This research aims to present the current forecasting techniques found in the literature, applied to predict time-series cancer incidence and then, compare these results with the current...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-55230-2 |
| _version_ | 1797274676326563840 |
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| author | Daniel Bouzon Nagem Assad Patricia Gomes Ferreira da Costa Thaís Spiegel Javier Cara Miguel Ortega-Mier Alfredo Monteiro Scaff |
| author_facet | Daniel Bouzon Nagem Assad Patricia Gomes Ferreira da Costa Thaís Spiegel Javier Cara Miguel Ortega-Mier Alfredo Monteiro Scaff |
| author_sort | Daniel Bouzon Nagem Assad |
| collection | DOAJ |
| description | Abstract The World Health Organization has highlighted that cancer was the second-highest cause of death in 2019. This research aims to present the current forecasting techniques found in the literature, applied to predict time-series cancer incidence and then, compare these results with the current methodology adopted by the Instituto Nacional do Câncer (INCA) in Brazil. A set of univariate time-series approaches is proposed to aid decision-makers in monitoring and organizing cancer prevention and control actions. Additionally, this can guide oncological research towards more accurate estimates that align with the expected demand. Forecasting techniques were applied to real data from seven types of cancer in a Brazilian district. Each method was evaluated by comparing its fit with real data using the root mean square error, and we also assessed the quality of noise to identify biased models. Notably, three methods proposed in this research have never been applied to cancer prediction before. The data were collected from the INCA website, and the forecast methods were implemented using the R language. Conducting a literature review, it was possible to draw comparisons previous works worldwide to illustrate that cancer prediction is often focused on breast and lung cancers, typically utilizing a limited number of time-series models to find the best fit for each case. Additionally, in comparison to the current method applied in Brazil, it has been shown that employing more generalized forecast techniques can provide more reliable predictions. By evaluating the noise in the current method, this research shown that the existing prediction model is biased toward two of the studied cancers Comparing error results between the mentioned approaches and the current technique, it has been shown that the current method applied by INCA underperforms in six out of seven types of cancer tested. Moreover, this research identified that the current method can produce a biased prediction for two of the seven cancers evaluated. Therefore, it is suggested that the methods evaluated in this work should be integrated into the INCA cancer forecast methodology to provide reliable predictions for Brazilian healthcare professionals, decision-makers, and oncological researchers. |
| first_indexed | 2024-03-07T15:02:38Z |
| format | Article |
| id | doaj.art-b759450fbcab4cf7a8c4bc7b4f831d39 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:02:38Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b759450fbcab4cf7a8c4bc7b4f831d392024-03-05T19:05:30ZengNature PortfolioScientific Reports2045-23222024-02-0114111810.1038/s41598-024-55230-2Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series modelsDaniel Bouzon Nagem Assad0Patricia Gomes Ferreira da Costa1Thaís Spiegel2Javier Cara3Miguel Ortega-Mier4Alfredo Monteiro Scaff5Department of Industrial Engineering, Universidade do Estado do Rio de JaneiroDepartment of Industrial Engineering, Universidade do Estado do Rio de JaneiroDepartment of Industrial Engineering, Universidade do Estado do Rio de JaneiroEscuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica De MadridEscuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica De MadridFundação Ary Frauzino para Pesquisa e Controle do CâncerAbstract The World Health Organization has highlighted that cancer was the second-highest cause of death in 2019. This research aims to present the current forecasting techniques found in the literature, applied to predict time-series cancer incidence and then, compare these results with the current methodology adopted by the Instituto Nacional do Câncer (INCA) in Brazil. A set of univariate time-series approaches is proposed to aid decision-makers in monitoring and organizing cancer prevention and control actions. Additionally, this can guide oncological research towards more accurate estimates that align with the expected demand. Forecasting techniques were applied to real data from seven types of cancer in a Brazilian district. Each method was evaluated by comparing its fit with real data using the root mean square error, and we also assessed the quality of noise to identify biased models. Notably, three methods proposed in this research have never been applied to cancer prediction before. The data were collected from the INCA website, and the forecast methods were implemented using the R language. Conducting a literature review, it was possible to draw comparisons previous works worldwide to illustrate that cancer prediction is often focused on breast and lung cancers, typically utilizing a limited number of time-series models to find the best fit for each case. Additionally, in comparison to the current method applied in Brazil, it has been shown that employing more generalized forecast techniques can provide more reliable predictions. By evaluating the noise in the current method, this research shown that the existing prediction model is biased toward two of the studied cancers Comparing error results between the mentioned approaches and the current technique, it has been shown that the current method applied by INCA underperforms in six out of seven types of cancer tested. Moreover, this research identified that the current method can produce a biased prediction for two of the seven cancers evaluated. Therefore, it is suggested that the methods evaluated in this work should be integrated into the INCA cancer forecast methodology to provide reliable predictions for Brazilian healthcare professionals, decision-makers, and oncological researchers.https://doi.org/10.1038/s41598-024-55230-2 |
| spellingShingle | Daniel Bouzon Nagem Assad Patricia Gomes Ferreira da Costa Thaís Spiegel Javier Cara Miguel Ortega-Mier Alfredo Monteiro Scaff Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models Scientific Reports |
| title | Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models |
| title_full | Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models |
| title_fullStr | Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models |
| title_full_unstemmed | Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models |
| title_short | Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models |
| title_sort | comparing the current short term cancer incidence prediction models in brazil with state of the art time series models |
| url | https://doi.org/10.1038/s41598-024-55230-2 |
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