CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Predicting Risk of Direct-to-Customer Drug Prescription using K-Mean Clustering Technique

by Francisca N. Ogwueleka, Timothy Moses
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 17
Year of Publication: 2015
Authors: Francisca N. Ogwueleka, Timothy Moses
10.5120/21635-4959

Francisca N. Ogwueleka, Timothy Moses . Predicting Risk of Direct-to-Customer Drug Prescription using K-Mean Clustering Technique. International Journal of Computer Applications. 121, 17 ( July 2015), 33-39. DOI=10.5120/21635-4959

@article{ 10.5120/21635-4959,
author = { Francisca N. Ogwueleka, Timothy Moses },
title = { Predicting Risk of Direct-to-Customer Drug Prescription using K-Mean Clustering Technique },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 17 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 33-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number17/21635-4959/ },
doi = { 10.5120/21635-4959 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:08:42.757210+05:30
%A Francisca N. Ogwueleka
%A Timothy Moses
%T Predicting Risk of Direct-to-Customer Drug Prescription using K-Mean Clustering Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 17
%P 33-39
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Exploration of patients medical record have necessitated the need for tracking customers with adverse drug reactions so as to deliver an analysis on risk involved and which cause of action proved effective. This paper acquires customer insights on adverse reactions experienced by taking anti-malaria drugs without medical diagnosis in north eastern part of Nigeria. The data collected were computed using k-means clustering algorithm implemented on Excel Visual Basic for Applications (VBA) Macro. Cluster values generated from the program was plotted. The graph predicts age of customers who are at risk of buying drugs without proper diagnosis and medical examination. Result obtained shows that about 38. 47% of working population are at risk of direct-drug prescription. The result implies that, there is tendency of low productivity and inefficiency among 38. 47% of the working force.

References
  1. Brownfield, E. D. , Bernhardt, J. M. , Phan, J. L. , Williams, M. V. , & Parker, R. M. (2004). Direct-to-customer drug advertisements on network television: an exploration of quantity, frequency and placement. J-health communication, 9, 491-497.
  2. Cao, X. , Maloney, K. B. , & Brusic, V. (2008). Data mining of cancer vaccine trials: a bird's eye view. Immune Research, 4, 1-11.
  3. Gledon, C, & Wayne, T. (2008). Understanding your customer: Segmentation techniques for gaining customer insight and predicting risk in Telecom Industry. AT&T Corporation (pp 1-14), Cary North Carolina: SAS Institute, Inc.
  4. Harleen, K, & Siri, K. W. (2006). Empirical study on applications of data mining techniques in healthcare. Journal of Computer Science, 2, 194-198.
  5. Kardi, T. (2007). K-Means clustering tutorial. Retrieved from http://people. revoledu. com/kardi/tutorial/kMean/
  6. Krista, W. J. (2012). Dangers of popular drugs used to cure malaria. Retrieved from http://www. thehealthierlife. co. uk/natural-health-articles Natural Health Article.
  7. Margaret, R. K. , Kevin, C. D. , & Ida, A. (2002). Data mining in healthcare information systems: case study of veterans' administration spinal cord injury population. IEEE Computer Society (pp 1-9), Hawaii: Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS'03).
  8. Shantakumar, B. P. , & Kumaraswamy, Y. S. (2009). Intelligent and effective heart attack prediction system using data mining and Artificial Neural Network. European Journal of Scientific Research, 3, 642-656.
  9. Tatonetti, P. N. , Patrick, P. Y. , Roxana, D. , & Russ, B. A. (2012). Data-driven prediction of drug effects and interactions. Clinical data analysis, science translational medicine, 4, 1-14.
  10. Thangavel, K. , Jaganathan, P. P. , & Easmi, P. O. (2006). Data mining approach to cervical cancer patients analysis using clustering technique. Asian Journal of Information Technology, 5, 413-417.
  11. Trading Economic (2015) Age dependency ratio (% of working-age population) in Nigeria. Retrieved from http://www. tradingeconomics. com/nigeria/age-dependency-ratio-percent-of-working-age-population-wb-data. html
  12. Tufte, E. (1997). Visual explanations, images and quantities, evidence and narrative. Journal of the American Statistics Association, 1, 1-2.
  13. Wynne, H. , Mong, L. L. , Bing, L. , & Tok, W. L. (2006). Exploration mining in diabetic patients databases: findings and conclusion. Association for Computing Machinery (pp 430-435), New York: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining.
  14. Wong, W. K. , Moore, A. , Cooper, G. , & Wagner, M. (2005). What's Strange About Recent Events (WSARE): An algorithm for early detection of disease outbreaks. Journal of Machine Learning Research, 6, 5-8.
Index Terms

Computer Science
Information Sciences

Keywords

Direct-to-customer drug prediction risk customer insight adverse reactions direct drug prescription.

Powered by PhDFocusTM