Analysis of Undergraduate Student's Knowledge of Self-Medication Practice using Machine Learning Algorithms
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 5 |
| Year of Publication : 2023 |
| Authors : Aruna Jothi Shanmugam, Hawa Ibrahim AbdAlla Hamid, Hamad Ghaleb Hassan Dailah, Sithara Begum .K, Saahira Banu Ahamed |
10.14445/23488379/IJEEE-V10I5P106
How to Cite?
Aruna Jothi Shanmugam, Hawa Ibrahim AbdAlla Hamid, Hamad Ghaleb Hassan Dailah, Sithara Begum .K, Saahira Banu Ahamed, "Analysis of Undergraduate Student's Knowledge of Self-Medication Practice using Machine Learning Algorithms," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 5, pp. 60-68, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I5P106
Abstract:
Self-medication (SM) is a medicinal product used to treat symptoms or self-medicate disorders identified by oneself. Overusing the medication recommended by a medical doctor for oneself or other family members (particularly when considering a child or an elder) falls into the description of SM. Treating medicine for oneself without specialist guidance might cause many side effects like prolonged disease course, drug resistance and complications. Even though informed SM is one method to decrease healthcare costs, inappropriate self-treatment poses different risks involving the recurrence of symptoms, drug resistance, drug side effects, etc. The study aims to examine the pervasiveness of SM in undergraduate students worldwide through a meta-analysis and systematic review of the study published on SM. A self-administered survey will collect data on demographic variables and practice SM to perform this study. The data is collected among undergraduate students from Aldayer University college, Jazan University, and KSA under three disciplines: English, maths, and nursing. The study aims to assess undergraduate students' knowledge regarding SM, identify drugs that students have taken without a prescription in the last six months, identify the reason for SM, identify sources of information about SM, and detect the sources of drugs utilized by students for SM. One hundred eighty samples were selected by purposive sampling per inclusive criteria at L Aldayer University College, Jazan University, KSA. The total number of samples is 180, of which 60 are collected from each discipline. In addition, machine learning-based support vector machine models can be used to generate predictive models to identify which studies are taking high SM. The experimental results stated that nursing college studies involve more SM practices than other undergraduate students.
Keywords:
Machine learning, Self-medication, Healthcare, Medication.
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