A Classification Model for Predicting Drug Side Effects by Age, Gender, and Race.

Author(s)	Dev Halvawala
Country	India
Abstract	The goal of this research project is to create a machine learning model that will categorize pharmacological adverse effects according to age, gender, and race. Personalized medicine is becoming more and more complex, thus it's critical to understand how various populations react to different treatments. The study uses a large dataset of user-reported side effects for analysis in an effort to find patterns and trends that can guide more individualized and successful treatment plans. The study makes use of a range of machine learning methods to evaluate how well they predict adverse effects, which will ultimately lead to better drug safety and effectiveness for a wider range of people.
Keywords	Drug Side Effects, Demographic Analysis, Machine Learning, Personalized Medicine, Data Classification, Feature Engineering, Health Informatics, Predictive Modeling, Healthcare Analytics.
Field	Computer
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-08-31
Cite This	A Classification Model for Predicting Drug Side Effects by Age, Gender, and Race. - Dev Halvawala - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.26952
DOI	https://doi.org/10.36948/ijfmr.2024.v06i04.26952
Short DOI	https://doi.org/gt9hbq

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International Journal For Multidisciplinary Research