Smoking Cessation through Support Vector Regression in a Mobile Application

Abstract: Smoking is the leading cause of early mortality in the world that may be prevented. However, there is a lack of evidence on the quality and efficacy of smartphone apps for smoking cessation. Mobile phone health interventions have made therapy more accessible than ever before thanks to the ubiquity of smartphones. Tobacco kills one person every six seconds. The use of machine learning techniques in the study assisted in reaching a conclusion on the cessation of tobacco consumption. Moreover, it provided a solid framework for dealing with cognitive dissonance via the use of a mobile application, among other things. This research article examines the relationship between the adoption of HCI and Support Vector Regression (SVR). Also, it makes use of K-means clustering to target specific groups of chain smokers. Furthermore, this study article delineates a comparison of the replacements that are now accessible in the application sector.

Keywords: HCI, Cognitive dissonance, Smoking cessation, Support vector regression, K-means clustering, application’s efficiency