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Health and Technology

, Volume 9, Issue 1, pp 37–44 | Cite as

Prediction of disease based on prescription using data mining methods

  • Shiva Kazempour Dehkordi
  • Hedieh SajediEmail author
Original Paper
  • 53 Downloads

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).

Keywords

Data mining Predicting disease Stacking learning algorithm 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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

Supplementary material

12553_2018_246_MOESM1_ESM.xlsx (1.9 mb)
ESM 1 (XLSX 1976 kb)

References

  1. 1.
    Zaki MJ, Meira W Jr. Data mining and analysis: fundamental concepts and algorithms. Cambridge: Cambridge University Press; 2014.CrossRefzbMATHGoogle Scholar
  2. 2.
    Han, Jiawei, Kamber, Micheline, “data mining: concepts and techniques”, Morgan Kaufmann; 2012.Google Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.Google Scholar
  5. 5.
    Taranu I. Data mining in healthcare: decision making and precision. Database Systems Journal. 2015;VI(4)Google Scholar
  6. 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. 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.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. 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.Google Scholar
  11. 11.
    S. Palaniappan, R. Awang, “Intelligent Heart Disease Prediction System Using Data Mining Techniques”. IJCSNS. 2008;Vol. 8, No. 8.Google Scholar
  12. 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. 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.Google Scholar
  14. 14.
    Schapire RE. The strength of weak learnability. J Mach Learn. 1990;5:197–227.Google Scholar
  15. 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.Google Scholar
  16. 16.
    M. Kantardzic, “Data Mining: Concepts, Methods¸ Models and Algorithms,” 2nd ed, Wiley; 2011.Google Scholar
  17. 17.
    Jolliffe IT. Principal component analysis. New York: Springer-Verlag; 1986.CrossRefzbMATHGoogle Scholar
  18. 18.
    Zhang SC. KNN-CF approach: incorporating certainty factor to kNN classification. IEEE Intelligent Informatics Bulletin. 2010;11(1):24–33.Google Scholar

Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics, Statistics and Computer Science, College of ScienceUniversity of TehranTehranIran

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