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Prediction of disease based on prescription using data mining methods

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Abstract

The volume of data being gathered every day is large and health care societies correspondingly generate a large volume of information daily. Although health care industry is rich in information but it requires discovering concealed relationships and patterns in data. The aim of this paper is employing data mining methods to find out knowledge in a dataset that was provided by a research center. By analyzing the drugs that were bought by each patient, our proposed method aims to predict the type of physician each patient has referred to and the type of disease he is suffering from. Our collected dataset contains details such as sex, age and the names of the drugs prescribed for each patient. For labeling the instances, a group of pharmacy students and professors has determined each patient’s disease. A number of experiments have been performed to compare the performance of different data mining techniques for predicting the diseases and the results illustrate that the proposed Stacking Model has higher accuracy compared to other data mining techniques such as k-Nearest Neighbor (kNN).

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Notes

  1. http://emri.tums.ac.ir

  2. https://rapidminer.com/

References

  1. Zaki MJ, Meira W Jr. Data mining and analysis: fundamental concepts and algorithms. Cambridge: Cambridge University Press; 2014.

    Book  MATH  Google Scholar 

  2. Han, Jiawei, Kamber, Micheline, “data mining: concepts and techniques”, Morgan Kaufmann; 2012.

  3. Liao SH, Chu PH, Hsiao PY. Data mining techniques and applications - a decade review from 2000 to 2011. Expert Syst Appl. 2012;39(12):11303–11.

    Article  Google Scholar 

  4. E. Barati et al., “A Survey on Utilization of Data Mining Approaches for Dermatological (Skin) Diseases Prediction,” Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Health Informatics (JSHI). 2011;March Edition.

  5. Taranu I. Data mining in healthcare: decision making and precision. Database Systems Journal. 2015;VI(4)

  6. Dean CA, Geneus CJ, Rice S, Johns M, Quasie-Woode D, Broom K, et al. Assessing the significance of health information seeking in chronic condition management. PEC J. 2017;100:1519–26.

    Google Scholar 

  7. Muellera N, Rojas-Ruedaa D, Basagañaa X, Cirach M, Cole-Hunter T, Dadvand P, et al. Health impacts related to urban and transport planning: a burden of disease assessment. Environ Int J. 2017;107:243–57.

    Article  Google Scholar 

  8. Metsker O, Bolgova E, Yakovlev A, Funkner A, Kovalchuk S. Pattern-based Mining in Electronic Health Records for complex clinical process analysis. Procedia Computer Science Journal. 2017;119:197–206.

    Article  Google Scholar 

  9. Chen J, Li K, Tang Z, Bilal K, Li K. A parallel patient treatment time prediction algorithm and its applications in hospital queuing-recommendation in a big data environment. IEEE ACCESS. 2016;4:1767–83.

    Article  Google Scholar 

  10. B. Abdelghani and E. Guven, “Predicting breast Cancer survivability using data mining techniques”, Ninth Workshop on Mining Scientific and Engineering Datasets in conjunction with the Sixth SIAM International Conference on Data Mining”, 2006.

  11. S. Palaniappan, R. Awang, “Intelligent Heart Disease Prediction System Using Data Mining Techniques”. IJCSNS. 2008;Vol. 8, No. 8.

  12. Repalli P. Prediction on diabetes using data mining approach, Oklahoma State University. Texas: Oklahoma State University, SCSUG Educational Forum Agenda; 2011.

    Google Scholar 

  13. Michael A. King, “ensemble learning techniques for structured and unstructured data”, dissertation for the degree of doctor of philosophy in business information technology, chapter 1, Virginia, United States, 2015.

  14. Schapire RE. The strength of weak learnability. J Mach Learn. 1990;5:197–227.

    Google Scholar 

  15. J. Sulzmann, J. Fürnkranz, “Rule stacking: an approach for compressing an ensemble of rule sets into a single classifier”, Discovery Science. 2011;pp 323–334. Springer Berlin Heidelberg.

  16. M. Kantardzic, “Data Mining: Concepts, Methods¸ Models and Algorithms,” 2nd ed, Wiley; 2011.

  17. Jolliffe IT. Principal component analysis. New York: Springer-Verlag; 1986.

    Book  MATH  Google Scholar 

  18. Zhang SC. KNN-CF approach: incorporating certainty factor to kNN classification. IEEE Intelligent Informatics Bulletin. 2010;11(1):24–33.

    Google Scholar 

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Correspondence to Hedieh Sajedi.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Dehkordi, S.K., Sajedi, H. Prediction of disease based on prescription using data mining methods. Health Technol. 9, 37–44 (2019). https://doi.org/10.1007/s12553-018-0246-2

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  • DOI: https://doi.org/10.1007/s12553-018-0246-2

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