Cohort Characteristics and Factors Associated with Cannabis Use among Adolescents in Canada Using Pattern Discovery and Disentanglement Method

• URL: http://arxiv.org/abs/2109.01739v1
• Date: Fri, 3 Sep 2021 21:53:14 GMT
• Title: Cohort Characteristics and Factors Associated with Cannabis Use among Adolescents in Canada Using Pattern Discovery and Disentanglement Method
• Authors: Peiyuan Zhou, Andrew K.C. Wong, Yang Yang, Scott T. Leatherdale, Kate Battista, Zahid A. Butt, George Michalopoulos, Helen Chen
• Abstract summary: We aim to discover significant frequent/rare associations of behavioral factors among Canadian adolescents related to cannabis use. We use a subset of Compass dataset which contains 18,761 records of students in grades 9 to 12 with 31 selected features (attributes) involving various characteristics. We then used the Pattern Discovery and Disentanglement (PDD) algorithm to detect strong and rare (yet statistically significant) associations from the dataset.
• Score: 3.77251737220697
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: COMPASS is a longitudinal, prospective cohort study collecting data annually from students attending high school in jurisdictions across Canada. We aimed to discover significant frequent/rare associations of behavioral factors among Canadian adolescents related to cannabis use. We use a subset of COMPASS dataset which contains 18,761 records of students in grades 9 to 12 with 31 selected features (attributes) involving various characteristics, from living habits to academic performance. We then used the Pattern Discovery and Disentanglement (PDD) algorithm that we have developed to detect strong and rare (yet statistically significant) associations from the dataset. PDD used the criteria derived from disentangled statistical spaces (known as Re-projected Adjusted-Standardized Residual Vector Spaces, notated as RARV). It outperformed methods using other criteria (i.e. support and confidence) popular as reported in the literature. Association results showed that PDD can discover: i) a smaller set of succinct significant associations in clusters; ii) frequent and rare, yet significant, patterns supported by population health relevant study; iii) patterns from a dataset with extremely imbalanced groups (majority class: minority class = 88.3%: 11.7%).

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