Citation

Ibrahim, Neveen and Foo, Lee Kien and Chua, Sook Ling (2023) Predicting the Severity of Adverse Events on Osteoporosis Drugs Using Attribute Weighted Logistic Regression. International Journal of Environmental Research and Public Health, 20 (4). p. 3289. ISSN 1660-4601

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Abstract

: Osteoporosis is a serious bone disease that affects many people worldwide. Various drugs have been used to treat osteoporosis. However, these drugs may cause severe adverse events in patients. Adverse drug events are harmful reactions caused by drug usage and remain one of the leading causes of death in many countries. Predicting serious adverse drug reactions in the early stages can help save patients’ lives and reduce healthcare costs. Classification methods are commonly used to predict the severity of adverse events. These methods usually assume independence among attributes, which may not be practical in real-world applications. In this paper, a new attribute weighted logistic regression is proposed to predict the severity of adverse drug events. Our method relaxes the assumption of independence among the attributes. An evaluation was performed on osteoporosis data obtained from the United States Food and Drug Administration databases. The results showed that our method achieved a higher recognition performance and outperformed baseline methods in predicting the severity of adverse drug events.

Item Type:	Article
Uncontrolled Keywords:	logistic regression; attribute weight; chi-square; osteoporosis disease; adverse drug events
Subjects:	R Medicine > RA Public aspects of medicine > RA421-790.95 Public health. Hygiene. Preventive medicine
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	11 Apr 2023 01:52
Last Modified:	11 Apr 2023 01:52
URII:	http://shdl.mmu.edu.my/id/eprint/11327

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