Indian Journal of Science and Technology
DOI: 10.17485/ijst/2020/v013i09/148136
Year: 2020, Volume: 13, Issue: 9, Pages: 1046-1056
Original Article
Abdulaziz Shehab 1,2*, Kamal Al dayah 2 and Ibrahim Elhenway 3
1Department of Computer Science, College of Science and Arts, Jouf University, Kingdom of Saudi Arabia
2Department of Information Systems, Faculty of Computers and information, Mansoura University, Egypt
3Department of Computer Science, Faculty of Computers and information, Zagazig University, Egypt
*Author for correspondence:
Abdulaziz Shehab
Department of Computer Science, College of Science and Arts, Jouf University, Kingdom of Saudi Arabia
E-mail ID: [email protected]
Background/objectives: Nowadays, there are thousands of approved drugs that can be used for treating people who have medical problems. Therefore, drug warnings and precautions are denoted to recognize a discrete set of adverse effects and other implied protection uncertainties that are useful for patient control.
Methods/analysis/findings: In this study, the intended framework is divided into two principal stages: data retrieval and data processing. Firstly, in the data collection stage, drug reports, drug interactions, malfunctions, number of deaths, and other factors had been obtained from various references, including RxNorm and Drug Bank using web service. Secondly, in the data processing phase, different data mining algorithms used to classify drugs into suitable drugs and non-suitable drugs.
Application/improvements: According to the experimental results, we found that the decision tree has more accuracy (97.9%) than other state-of-art methods.
Keywords: Drug Interactions, Drugs Classification, Naïve Bayes, Support Vector Machine, Decision Tree.
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