Objective: In the last few decades, the consumption of cannabis-based products for recreational purposes
has dramatically increased. Unfortunately, cannabis consumption has been associated with the incidences
of cardiovascular diseases. Hence, there is a necessity for understanding the plausible mechanics of
cardiophysiological changes due to cannabis consumption. Accordingly, the current study was designed
to understand the suitability of the recurrence quantification analysis (RQA) method in detecting the
changes in the heart rate variability (HRV) time-series signals due to the consumption of cannabis
(bhang). Further, a machine learning model has been proposed for the automated detection of the
cannabis takers.
Materials and Methods: The RQA of the HRV time-series signals from 200 healthy Indian male paddy-field
workers were carried out. The obtained parameters were statistically analyzed using the Mann-Whitney
U test. Further, the decision trees, weight-based feature ranking, and dimensionality reduction methods
were employed for identifying the relevant features for the development of a suitable machine learning
model.
Results: Observable changes in the patterns of the recurrence plots among the bhang consuming and nonconsuming groups were noticed. However, there were no significant differences in the RQA parameters.
Among the developed machine learning models, the SVM model obtained from the “Information gain
ratio” feature selection method exhibited the highest accuracy (%) of 69.09 ± 9.33.
Conclusion: Our study suggests that the RQA method is not as effective as the time and frequency domain
methods for detecting the alterations in the HRV time-series signals due to cannabis consumption. The
SVM model was found to be the best model for the automated detection of cannabis takers. The selection of the features by the information gain ratio method played an important role in the development of the
optimized SVM model.