An Ensemble Model for Detection of Adverse Drug Reactions

Keywords: Adverse drug reactions, Classification, Ensemble Model, Machine Learning, Pointwise Mutual Information

Abstract

The detection of adverse drug reactions (ADRs) plays a necessary role in comprehending the safety and benefit profiles of medicines. Although spontaneous reporting stays the standard approach for ADR documents, it suffers from significant under reporting rates and limitations in terms of treatment inspection. This study proposes an ensemble model that combines decision trees, support vector machines, random forests, and adaptive boosting (ADA-boost) to improve ADR detection. The experimental evaluation applied the benchmark data set and many preprocessing techniques such as tokenization, stop-word removal, stemming, and utilization of Point-wise Mutual Information. In addition, two term representations, namely, term frequency-inverse document frequency and term frequency, are utilized. The proposed ensemble model achieves an F-measure of 89% on the dataset. The proposed ensemble model shows its ability in detecting ADR to be a favored option in achieving both accuracy and clarity.

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Author Biographies

Ahmed A. Nafea, Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, 31001, Ramadi, Anbar, Iraq

Ahmed Adil Nafea is a Lecturer at the Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, Iraq. He got the B.Sc. degree in Information System and the M.Sc. degree in Artificial Intelligence. His research interests are in Artificial Intelligence, Machine Learning, Deep learning, and Natural Language Processing. Mr. Ahmed is an Honorary member of the Golden Key International Honour Society: in Atlanta, GA, US.

Mustafa S. Ibrahim, Department of Computer Science, University of Anbar, 31001, Ramadi, Anbar, Iraq

Mustafa S. Ibrahim is a Lecturer at the Department of Computer Science, University of Anbar Ramadi, Iraq. He got the B.Sc. degree in Computer Science and the M.Sc. degree in Computer Science. His research interests are in Artificial Intelligence, Machine Learning, and Deep learning.

Abdulrahman A. Mukhlif, Registration and Students Affairs, University Headquarter, University of Anbar, 31001, Ramadi, Anbar, Iraq

Abdulrahman A. Mukhlif is a Lecturer at the Department of Registration and Students Affairs, University Headquarter, University of Anbar, Iraq. He got the B.Sc. degree in Information System and the M.Sc. degree in Computer Science. His research interests are in Artificial Intelligence, Machine Learning, and Deep learning.

Mohammed M. AL-Ani , Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia

Mohammed M AL-Ani is a Lecturer at the Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, Iraq. He got the B.Sc. degree in Information System and the M.Sc. degree in Artificial Intelligence. His research interests are in Artificial Intelligence, Machine Learning, Deep learning, and Natural Language Processing. Mr. Mohammed is an Honorary member of the Golden Key International Honour Society: in Atlanta, GA, US.

Nazlia Omar, Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia

Nazlia Omar is an Associate Professor at the Faculty of Information Science and Technology at Universiti Kebangsaan Malaysia (UKM). She received the B.Sc. degree in Computer Science, the M.Sc. degree in Information Systems and the Ph.D. degree in Natural Language Processing. Her research interests are in Natural Language Processing, with particular focus on Malay, English and Arabic language processing issues. Dr. Nazlia is a member of the Center for Artificial Intelligence Technology (CAIT), FTSM.

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Published
2024-02-20
How to Cite
Nafea, A. A., Ibrahim, M. S., Mukhlif, A. A., AL-Ani , M. M. and Omar, N. (2024) “An Ensemble Model for Detection of Adverse Drug Reactions”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 12(1), pp. 41-47. doi: 10.14500/aro.11403.