## 5 Conclusions  
The occurrence of cardiovascular diseases in cannabis users is increasing day by day. This demands that the alteration in the physiology of the CAR (the primary regulator of heart activity) is recognized and that automated cannabis user identification tools are developed. The current study attempted to detect the alteration in the CAR activity due to regular cannabis intake. The HRV signals obtained from 200 Indian male paddy-field workers were subjected to three popular decomposition methods, namely, EMD, DWT, and WPD. The extraction of entropy-based parameters was carried out from the IMFs \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Performance Measures** & **Description** & **Ref.** \\ \hline Accuracy & Accuracy represents one of the most popular performance evaluation tools. It can be obtained by dividing the number of accurately classified observations by the total number of observations done by the classification model. Mathematically, accuracy is given by Equation (23). \\ Accuracy & Accuracy \(=\frac{TP+TN}{TP+TP+TN+FN}\) where _TP_, _TN_, _FP_, and _FN_ refer to the true positive value, the true negative value, the false positive value, and the false-negative value, respectively. A true positive is an outcome when the model accurately predicts the positive class. A true negative is a result for which the model provides an accurate prediction of the negative class. If the model mistakenly predicts a positive class, this is called a false positive. When the model erroneously forecasts the negative class, this is known as a false negative. \\ \hline AUC & AUC refers to the total area under the receiver operating characteristics (ROC) curve. The ROC curve represents a probability curve that indicates the performance of an ML model in differentiating between the classes at all classification thresholds. The value of AUC describes the degree of separability of the classification model. \\ \hline Precision & Precision represents the ratio of accurately classified positive observations to the number of observations classified as positive (Equation (24)). It is also regarded as the positive predictive value. \\ Precision & Precision \(=\frac{TP}{TP+TP}\) where _TP_ and _FP_ refer to the true positive value and the false positive value, respectively. \\ \hline Sensitivity & Sensitivity refers to the ratio of accurately classified positive observations to the total number of positive observations (Equation (25)). It is also called true positive rate (TPR) and recall. \\ Sensitivity & Sensitivity \(=\frac{TP}{TP+TN}\) where _TP_ and _FN_ refer to the true positive value and the false negative value, respectively. \\ \hline F-measure & F-measure represents the harmonic mean of precision and sensitivity (Equation (26)). Its value can vary from 0 to 1 and is nearly the same as accuracy (in %). \\ F-measure & F \(-\) measure \(=2\ \times\ \frac{Precision\times Sensitivity}{Precision+ Sensitivity}=\frac{2\times TP}{2\times TP+FP+FN}\) where _TP_, _FP_, and _FN_ refer to the true positive value, the false positive value, and the false-negative value, respectively. \\ \hline Specificity & Specificity corresponds to the ratio of accurately classified negative observations to the total number of negative observations (Equation (27)). \\ Specificity & Specificity \(=\frac{TN}{TN+TP}\) where _TN_ and _FP_ refer to the true negative value and the false positive value, respectively. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance measures of classification models. and the wavelet coefficients. The statistical analysis of the entropy parameters using the Mann-Whitney test suggested a significant variation of the entropy parameters between the control and the bhang-consuming group. This fact suggested a change in CAR activity caused by cannabis intake. The present study also attempted to propose an ML model for the automated recognition of the bhang consuming population.