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Determining Smokers’ Quitting Behavior Patterns for Multi-level Intervention of a Smoking Cessation Program

Dennis A. Martillano 1, Argel Pereña Barrameda 2, Katherine Joy Mendoza Domondon 2, and John Kenneth Leuterio Rioflorido 2
1. College of Computer and Information Science, Malayan Colleges Laguna, Philippines
2. Malayan Colleges Laguna, Philippines

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

Cite: 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

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.