Machine Learning Analysis to Identify Factors Associated with Requesting Tobacco Cessation Services Among Users of an Online Self-Diagnostic Questionnaire in Mexico.
crossref(2024)
Abstract
Abstract Introduction: While traditional cessation services exist, internet-based options are gaining popularity. However, understanding online users' unique characteristics compared to traditional users is crucial. This study utilize machine learning (ML), aimed to identify these online users and their needs. Method: Through analyzing 14,182 records of adults who completed online nicotine dependence screening questionnaire, a random forest algorithm plus oversampling was used to predict request services. Results: The algorithm accurately identified 78.6% of users and rejected 68.8% of non-users. Notably, age, sex, dependence severity indicators, certain locations, and even specific occasions like World No Tobacco Day, were identified as key factors influencing service request. Discussion: These findings suggest the effectiveness of random forest algorithm in predicting potential users. Moreover, the predictor variables offer valuable insights for crafting targeted prevention and awareness campaigns, potentially leading to improved campaign effectiveness and ultimately, helping more individuals seeking cessation support.
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