College of Computer and Information Science
Journal of Advances in Information Technology
Vol. 11, No. 4, November 2020
Determining Smokers’ Quitting Behavior Patterns for Multi-level Intervention of a Smoking Cessation Program
Dennis A. Martillano
College of Computer and Information Science, Malayan Colleges Laguna, Philippines
Email: [email protected]
Argel Pereña Barrameda, Katherine Joy Mendoza Domondon, and John Kenneth Leuterio Rioflorido
Malayan Colleges Laguna, Philippines
Email: {argel.barrameda, katherine.domondon, johnkenneth.rioflorido}@gmail.com
Abstract— Smoking Cessation is the medical process where a smoker undergoes a range of procedures to quit smoking. Related studies observe that a smokers’ ability to quit was dependent on patterns to a smoker’s behavior. With this, the study aims to develop a model that determines quitting behavior patterns of smokers undergoing cessation. Smoking Cessation dataset was acquired from an identified municipal cessation center in the Philippines. The dataset was subjected to Classification via Clustering technique, to identify different classes/groups of quitting patterns, utilizing attributes related to smoking behavior. Results reveal four (4) distinct clusters of quitting patterns with the consideration of the Elbow Method, which then underwent proper Behavioral Pattern labeling, through the guidance of a public health expert. These labels were included as an additional attribute in the final dataset before classifying. The final model was integrated into the developed web application for Smoking Cessation Center website, which enables public health officers and medical practitioners in predicting the smokers’ quitting behavior pattern.
Index Terms— medical data mining, classification via clustering, elbow methodology, smoking cessation
Citation— Dennis A. Martillano, Argel Pereña Barrameda, Katherine Joy Mendoza Domondon, and John Kenneth Leuterio Rioflorido, “Determining Smokers’ Quitting Behavior Patterns for Multi-level Intervention of a Smoking Cessation Program,” Journal of Advances in Information Technology, Vol. 11, No. 4, pp. 200-208, November 2020. doi: 10.12720/jait.11.4.200-208