Analysis of heart rate variability to understand the effect of cannabis consumption on Indian male paddy-field workers. (September 2020) - Nayak, Suraj K.; Pradhan, Bikash K.; Banerjee, Indranil; Pal, Kunal
Highlights: HRV analysis is proposed to understand the effect of bhang (an Indian cannabis preparation) consumption on ANS activity. A reduction in the HRV in bhang-consuming people was observed as compared to the control group. A reduction in the HRV parameters in bhang-consuming group was observed as compared to the control group. A plethora of machine learning models was employed for automated identification of cardiovascular changes. The GBT model may help in the early prognosis of cardiovascular changes in the bhang-consuming population. Abstract: The consumption of cannabis-based products is increasing worldwide day-by-day because of their euphoric effects. Numerous studies have reported the incidence of cardiovascular diseases and even mortality in people consuming cannabis. However, not much attention has been paid to understand the cannabis-induced alteration in the autonomic nervous system (ANS) activity, which can help in the early diagnosis of cardiovascular diseases. The current study investigated the alteration in the ANS activity of 200 Indian male volunteers due to the consumption of bhang (a cannabis-based product) using heart rate variability (HRV) analysis. The results suggested a reduction in the variability of the heart rate, increased sympathetic dominance, and a corresponding reduction in the parasympathetic activity in the bhang consuming population, which may lead to various cardiovascular diseases. These inferences can act as evidence for counseling people to stop consuming cannabis. The study further proposes a machine learning model for automated identification of the bhang consuming population. The HRV parameters were subjected to weight-based feature ranking and dimension reduction methods to select suitable inputs for the machine learning models. After comparing the performances of the Naïve Bayes (NB), Generalized Linear Model (GLM), Linear Regression (LR), Fast Large Margin (FLM), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), Gradient Boosted Tree (GBT), and Support Vector Machine (SVM), a GBT model was finally chosen as the best model.