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

by Francisca N. Ogwueleka, Timothy Moses
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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
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Index Terms

Computer Science
Information Sciences

Keywords

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

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