Abstract: Diagnosing and monitoring drug use creates important challenges in the medical and social fields. Traditional methods have relied heavily on self-reports, which can be unreliable due to various factors such as social bias and memory bias. In recent years, there has been a growing interest in using machine learning techniques to augment or replace traditional approaches to drug detection. This paper provides a comprehensive overview of the current state of the art in machine learning-based drug use diagnosis.
Describes common preprocessing steps for cleaning and preparing data for analysis, including feature extraction and dimensionality reduction techniques.
We then take a closer look at various machine learning algorithms and models used for drug detection, including random forests, deep learning architectures, and ensemble techniques. We discuss the strengths and weaknesses of each approach and highlight recent advances and challenges.
Additionally, we discuss ethical considerations for using machine learning in this context, including privacy concerns, algorithmic bias, and the impact of false positives and negatives.
Finally, we identify potential avenues for future research, including developing more robust and interpretable models, integrating multiple data methods to improve accuracy, and exploring real-time monitoring systems.
Overall, this review highlights the potential of machine learning to revolutionize drug use diagnosis and highlights the importance of interdisciplinary collaboration to address the complex challenges inherent in this field.
Cite:
Ajay Shetty, Chirag V, Darshan U Shetty, Disha P, Dr. Babu Rao K, "DRUG CONSUMPTION DETECTION USING MACHINE LEARNING", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13372.
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DOI:
10.17148/IJARCCE.2024.13372
