Application of machine learning to predict the influence of drug abuse on individuals.
- Author
- Chakururama Audrey
- Title
- Application of machine learning to predict the influence of drug abuse on individuals.
- Abstract
-
ABSTRACT
This study explores the application of machine learning (ML) techniques to predict the influence of drug abuse on individuals. The aim is to develop predictive models that can identify individuals at risk of drug abuse based on their personality traits. Using data from diverse sources such as demographic information and medical records, ML algorithms are trained to recognize patterns associated with drug abuse susceptibility. The study focuses on leveraging the Big Five personality traits (Extraversion, Openness to Experience, Conscientiousness, Neuroticism, and Agreeableness) as predictive features, alongside other relevant variables. Through feature selection, model validation, and interpretation techniques, the study aims to create accurate and interpretable models capable of early detection and personalized intervention. Ethical considerations, including privacy protection and bias mitigation, are also addressed. The findings of this research have implications for healthcare providers, policymakers, and community organizations in developing targeted strategies for drug abuse prevention and intervention. Overall, the study contributes to advancing our understanding of how ML can be harnessed to mitigate the impact of drug abuse on individuals and society.
- Date
- JUNE 2024
- Publisher
- BUSE
- Keywords
- machine learning (ML) techniques, drug abuse, Extraversion, Openness to Experience, Conscientiousness, Neuroticism, and Agreeableness
- Supervisor
- Mr Chaitezvi
- Item sets
- Department of Computer Science
- Media
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Chakururama Audrey
Part of Application of machine learning to predict the influence of drug abuse on individuals.