Biomedical Statistics and Informatics

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Cancer Cases in Kenya; Forecasting Incidents Using Box & Jenkins Arima Model

Received: 13 December 2016    Accepted: 17 January 2017    Published: 15 February 2017
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Abstract

The aim of the study was to fit appropriate time series models in assessing the accuracy of the Box Jenkins and ARIMA model in forecasting of Cancer case admissions for all people of any age from different health facilities across the country. Box-Jenkins was selected for evaluation because it has the potential of producing a point forecast within a given population, it provides a forecast interval, and is based upon a proven model. Forecast results and their associated forecast intervals may help Health facilities and health practitioners make informed decisions about whether the number of observed cancer reports in a given timeframe represents a potential incidence or is a function of random variation. Data management and analysis were done in SPSS Software. The data was segmented into two sets: Training Set (from 2000 to 2015) and the Test Set (from 2016 to 2018). The hold out set (test) provides the gold standard for measuring the model’s true prediction error which refers to how well the model forecasts for new data. To note, the test data were only be used after a definitive model has been selected. This was to ensure unbiased estimates of the true forecast error. The results were presented in form of tables, graphs and context. In this study, the developed model for cancer case incidents in Kenya was found to be an ARIMA (2,1,0). From the forecast available by using the developed model, it can be seen that forecasted incidents for the year 2015-16 is higher than 2014-15 and in later years the incidents increases. The model can be used by researchers for forecasting of cancer incidents in Kenya.

DOI 10.11648/j.bsi.20170202.11
Published in Biomedical Statistics and Informatics (Volume 2, Issue 2, June 2017)
Page(s) 37-48
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Box-Jenkins, ARIMA Models, Forecasting, Cancer Incidence

References
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Author Information
  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Mathematics and Informatics, Kabarak University, Nakuru, Kenya

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  • APA Style

    Amos Langat, George Orwa, Joel Koima. (2017). Cancer Cases in Kenya; Forecasting Incidents Using Box & Jenkins Arima Model. Biomedical Statistics and Informatics, 2(2), 37-48. https://doi.org/10.11648/j.bsi.20170202.11

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    ACS Style

    Amos Langat; George Orwa; Joel Koima. Cancer Cases in Kenya; Forecasting Incidents Using Box & Jenkins Arima Model. Biomed. Stat. Inform. 2017, 2(2), 37-48. doi: 10.11648/j.bsi.20170202.11

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    AMA Style

    Amos Langat, George Orwa, Joel Koima. Cancer Cases in Kenya; Forecasting Incidents Using Box & Jenkins Arima Model. Biomed Stat Inform. 2017;2(2):37-48. doi: 10.11648/j.bsi.20170202.11

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  • @article{10.11648/j.bsi.20170202.11,
      author = {Amos Langat and George Orwa and Joel Koima},
      title = {Cancer Cases in Kenya; Forecasting Incidents Using Box & Jenkins Arima Model},
      journal = {Biomedical Statistics and Informatics},
      volume = {2},
      number = {2},
      pages = {37-48},
      doi = {10.11648/j.bsi.20170202.11},
      url = {https://doi.org/10.11648/j.bsi.20170202.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.bsi.20170202.11},
      abstract = {The aim of the study was to fit appropriate time series models in assessing the accuracy of the Box Jenkins and ARIMA model in forecasting of Cancer case admissions for all people of any age from different health facilities across the country. Box-Jenkins was selected for evaluation because it has the potential of producing a point forecast within a given population, it provides a forecast interval, and is based upon a proven model. Forecast results and their associated forecast intervals may help Health facilities and health practitioners make informed decisions about whether the number of observed cancer reports in a given timeframe represents a potential incidence or is a function of random variation. Data management and analysis were done in SPSS Software. The data was segmented into two sets: Training Set (from 2000 to 2015) and the Test Set (from 2016 to 2018). The hold out set (test) provides the gold standard for measuring the model’s true prediction error which refers to how well the model forecasts for new data. To note, the test data were only be used after a definitive model has been selected. This was to ensure unbiased estimates of the true forecast error. The results were presented in form of tables, graphs and context. In this study, the developed model for cancer case incidents in Kenya was found to be an ARIMA (2,1,0). From the forecast available by using the developed model, it can be seen that forecasted incidents for the year 2015-16 is higher than 2014-15 and in later years the incidents increases. The model can be used by researchers for forecasting of cancer incidents in Kenya.},
     year = {2017}
    }
    

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    AU  - Amos Langat
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    AU  - Joel Koima
    Y1  - 2017/02/15
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    AB  - The aim of the study was to fit appropriate time series models in assessing the accuracy of the Box Jenkins and ARIMA model in forecasting of Cancer case admissions for all people of any age from different health facilities across the country. Box-Jenkins was selected for evaluation because it has the potential of producing a point forecast within a given population, it provides a forecast interval, and is based upon a proven model. Forecast results and their associated forecast intervals may help Health facilities and health practitioners make informed decisions about whether the number of observed cancer reports in a given timeframe represents a potential incidence or is a function of random variation. Data management and analysis were done in SPSS Software. The data was segmented into two sets: Training Set (from 2000 to 2015) and the Test Set (from 2016 to 2018). The hold out set (test) provides the gold standard for measuring the model’s true prediction error which refers to how well the model forecasts for new data. To note, the test data were only be used after a definitive model has been selected. This was to ensure unbiased estimates of the true forecast error. The results were presented in form of tables, graphs and context. In this study, the developed model for cancer case incidents in Kenya was found to be an ARIMA (2,1,0). From the forecast available by using the developed model, it can be seen that forecasted incidents for the year 2015-16 is higher than 2014-15 and in later years the incidents increases. The model can be used by researchers for forecasting of cancer incidents in Kenya.
    VL  - 2
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