Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 30, 24 January 2024
Open
Access | Article
The research of analysis lung, bronchus and trachea cancer death rate in US
* Author to whom correspondence should be addressed.
Abstract
This research delves into an analysis of lung, bronchus, and trachea cancer rates in the United States across genders. Employing the data spanning seven decades (1950-2020) sourced from the Our World in Data website, the study leverages time series modeling techniques, ARIMA and ETS models. The ARIMA methodology initiates with an assessment of data stationarity, followed by differencing procedures to transform the dataset into a non-stationary data. Subsequently, Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots are examined. Last, the ARIMA model is fitted to dissect the mortality rates among males and females. Simultaneously, the ETS model is directly applied to the mortality data of both genders. The components of the ETS model and the check residuals for ETS are delineated. The outcomes reveal the trends: both genders exhibit a discernible decline in lung, bronchus, and trachea cancer death rates over the period. Despite this downward trajectory, the persistent mortality rates underscore the gravity of the issue. This paper advocates for a heightened focus on lung-related cancers. Understanding and addressing these mortality rates are imperative.
Keywords
Lung, bronchus and trachea cancer death rate, ETS model, ARIMA model, Time series
References
1. Siegel R L, et al. 2021 Cancer statistics. Ca Cancer J Clin, 71(1), 7-33.
2. Adjei A A 2019 Lung cancer worldwide. Journal of Thoracic Oncology, 14(6), 956.
3. Sharma P, et al. 2019 Emerging trends in the novel drug delivery approaches for the treatment of lung cancer. Chemico-biological interactions, 309, 108720.
4. Malhotra J, et al. 2016 Risk factors for lung cancer worldwide. European Respiratory Journal, 48(3), 889-902.
5. Witschi H 2001 A short history of lung cancer. Toxicological sciences, 64(1), 4-6.
6. Alberg A J, Brock M V and Samet J M 2005 Epidemiology of lung cancer: looking to the future. Journal of clinical oncology, 23(14), 3175-3185.
7. Dubey A K, Gupta U and Jain S 2016 Epidemiology of lung cancer and approaches for its prediction: a systematic review and analysis. Chinese journal of cancer, 35(1), 1-13.
8. Tammemägi M C 2018 Selecting lung cancer screen using risk prediction models-where do we go from here. Translational lung cancer research, 7(3), 243.
9. Chen Y, et al. 2021 The function of LncRNAs and their role in the prediction, diagnosis, and prognosis of lung cancer. Clinical and translational medicine, 11(4), e367.
10. Spitz M R, et al. 2007 A risk model for prediction of lung cancer. Journal of the National Cancer Institute, 99(9), 715-726.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).
- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-283-1
- ISBN (Online)
- 978-1-83558-284-8
- Published Date
- 24 January 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/30/20241024
- Copyright
- 24 January 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited