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score of 81.9 ± 12.5%, with 80.8% accuracy, 80.8% sensitivity, and 85.2% specificity). Therefore, the SVM model was considered suitable for classifying drug and nondrug users' urine samples. These findings underscore the promise of ATR-FTIR spectroscopy combined with machine learning for the rapid detection of drugs in urine samples. This innovative technique potentially improves the drug screening process. However, we recommend that the current findings be interpreted as preliminary. Future studies are necessary to increase the sample size, create a comprehensive library of standard spectral data for various narcotics to aid in identification, and optimize the models to enhance the accuracy of classifying drug users' urine samples.
Lwin et al. (Fri,) studied this question.