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Performance of Autoregressive Tree Model in Forecasting Cancer Patients

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Strategic System Assurance and Business Analytics

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Abstract

In this paper, our aim is to study the time series data on cancer patients of Punjab, a state of India, by applying the autoregressive tree (ART) model. ART model is an extension of AR model which provides a superior predictive accuracy as compared to AR. In ART model, at first predicted variable and target variable are to be decided on the basis of our own data. And the second step is to form a decision tree for our target variable by transforming the data. This approach of AR model is very much effective for forecasting and a very few research work had been done up to date with ART model. We choose this region for our research purpose as Punjab is considered as a cancer capital of India due to its high increasing rate of cancer-affected patients. In this paper, our goal is to forecast the future trend of cancer patients of Punjab and a case study of cancer incidence is also analyzed and forecast trends of common cancer types among male, female, and both of them. A hypothesis test and calculating our forecast result are also provided in this paper on the basis of mean forecast error and mean square forecast error. The purpose of this study is to aware the people and various organizations regarding the frightened issue of this region and our results could provide appropriate planning regarding eradicate this disease.

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References

  1. Weiss CH (2018) An introduction to discrete valued time series, Wiley

    Google Scholar 

  2. Box GEP, Jenkins GM, Riesel GC (2008) Time series analysis: forecasting and control, Prentice Hall

    Google Scholar 

  3. Williams MB (2001) Multivariate vehicular traffic flow prediction: evaluation of ARIMAX modeling. Transp Res Rec 1776:30332–30355

    Article  Google Scholar 

  4. Andrews BH, Dean DM, Swainn R, Cole C (2013) Building ARIMA and ARIMAX models for predicting long-term disability benefit application rates in the public/private sectors. Society of actuaries health section, University of Southern Maine

    Google Scholar 

  5. Beard E, West R, Michie S, Brown J (2017) Association between smoking and alcohol-related behaviors: a time series analysis of population trends in England. Soc Study Addict 112:1832–1841

    Article  Google Scholar 

  6. Neshat N, Hadian H, Behzad M (2018) Nonlinear ARIMAX model for long–term sectoral demand forecasting. Manag Sci Lett 8:581–592

    Article  Google Scholar 

  7. Jing QL, Cheng Q, Marshall JM, Hu WB, Yang ZC, Lu JH (2018) Imported cases and minimum temperature drive dengue transmission in Guangzhou, China: evidence from ARIMAX model. Epidemiol Infect 146:1226–1235

    Article  Google Scholar 

  8. Hamilton DJ (1994) Time series analysis. Princeton University Press, Princeton. New Jersey, pp 257–349

    Google Scholar 

  9. Chulia H, Gupta R, Uribe MJ, Wohar EM (2016) Impact of US uncertainties on emerging and mature markets: evidence from a quantile vector autoregressive approach. J Int Financ Mark, Institutions Money

    Google Scholar 

  10. Cavalcante L, Bessa JR, Reis M, Browell J (2017) LASSO vector autoregressive structure for very short-term wind power forecasting. Wind Energy 20:657–675

    Article  Google Scholar 

  11. Zhao Y, Pinson P, Tang Y (2018) Correlation constrained and sparsity-controlled vector autoregressive model for spatio-temporal wind power forecasting. IEEE Trans Power Syst

    Google Scholar 

  12. Sales JD, Delleur JW, Yevjevich V, Lane WL (1997) Applied modeling of hydrologic time series. Water Resources Publication, LLC

    Google Scholar 

  13. Novales A, de Fruto RF (1997) Forecasting with periodic models: a comparison with time invariant coefficient models. Int J Forecast 13:393–405

    Article  Google Scholar 

  14. Voyant C, Gooijer DGJ, Notton G (2018) Periodic autoregressive forecasting of global solar irradiation without knowledge based model implementation. Sol Energy 174:121–129

    Article  Google Scholar 

  15. Cucina D, Rizzo M, Ursu E (2018) Multiple change point detection for periodic autoregressive models with an application to river flow analysis

    Google Scholar 

  16. Dewald SL, Lewis P (1985) A new laplace second-order autoregressive time series model NLAR (2). IEEE Trans Inf Theory 31:645–651

    Article  Google Scholar 

  17. Jose KK, Thomas MM (2012) A product autoregressive model with log-laplace marginal distribution. Statistica 3:317–336

    Google Scholar 

  18. Kuttykrishnan A. P. (2006) Laplace Autoregressive time series models. Department of Statistics, University of Calicut

    Google Scholar 

  19. Li L, Zhang X (2010) Study of data mining algorithm based on decision tree. In: International conference on computer design and applications 1

    Google Scholar 

  20. Song YY, Lu Y (2015) Decision tree methods: applications for classification and prediction. Biostat Psychiatry 27

    Google Scholar 

  21. Cheng Z, Nakatsugawa M, Hu C, Robertson SP, Hui X, Moore JA, Muse M (2018) Evaluation of classification of regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy. Adv Radiat Oncol 3:346–355

    Article  Google Scholar 

  22. Momenyan S, Baghestani AR, Momenyan N, Naseri P, Akbari ME (2018) Survival prediction of patients with breast cancer: comparisons of decision tree and logistic regression analysis. Int J Cancer Manag

    Google Scholar 

  23. Meek C, Chickering DM, Heckerman D (2002) Autoregressive tree models for time series analysis. In: Conference paper

    Google Scholar 

  24. Chaudhary K, Luthara UK (2015) 50 years of cancer control in India. Cancer registration in India. Article

    Google Scholar 

  25. Saranath D, Khanna A (2014) Current status of cancer burden: global and Indian scenario. Biomed Res J 1:1–5

    Article  Google Scholar 

  26. Sarma P, Sharma P, Singh R, Mishra A, Kumar H, Bhattacharya A, Harikrishnareddy P, Singh A, Medhi B (2018) Status of cancer research in India. Proc Indian Natl Sci Acad 84:141–152

    Google Scholar 

  27. Baldwe RA, Dikshit R, Laversanne M M, Fredie B (2014) Cancer incidence trends in India. Jpn J Clin Oncol 44:401–407

    Article  Google Scholar 

  28. Ali I, Wani WA (2011) Cancer Scenario in India with Future Prespectives. Cancer Ther 8:56–70

    Google Scholar 

  29. Murray CJ, Lopez AD (1996) Global health status in developing countries: global burden of diseases and injuries in SEARO (Harvard School of Public Health) 1 & 2

    Google Scholar 

  30. Thakur JS, Rao BT, Rajwanshi A, Parwana HK, Kumar R (2008) Epidemiological study of high cancer among rural agricultural community of Punjab in Northern India. Int J Environ Res Public Health 5:399–407

    Article  Google Scholar 

  31. Labani S, Asthana S, Sultan A (2015) Cancer in Punjab: evidence from cancer atlas. Indian J Community Health 27:295–297

    Google Scholar 

  32. Bal MS, Bodal VK, Kaur J, Kaur M, Sharma S (2015) Patterns of cancer: a study of 500 Punjabi patients. Asian Pac J Cancer Prev 16:5107–5110

    Article  Google Scholar 

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Authors and Affiliations

  1. Guru Kashi University, Bathinda, Punjab, India

    Sukhpal Kaur & Madhuchanda Rakshit

Authors
  1. Sukhpal Kaur
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  2. Madhuchanda Rakshit
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Corresponding author

Correspondence to Madhuchanda Rakshit .

Editor information

Editors and Affiliations

  1. Amity Center for Interdisciplinary Research, Amity University, Noida, India

    P. K. Kapur

  2. Department of Operational Research, University of Delhi, New Delhi, Delhi, India

    Ompal Singh

  3. Amity University, Tashkent, Uzbekistan

    Sunil Kumar Khatri

  4. Western Norway University of Applied Sciences, Haugesund, Norway

    Ajit Kumar Verma

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Kaur, S., Rakshit, M. (2020). Performance of Autoregressive Tree Model in Forecasting Cancer Patients. In: Kapur, P.K., Singh, O., Khatri, S.K., Verma, A.K. (eds) Strategic System Assurance and Business Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-15-3647-2_15

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