Applicability of Data Mining and Predictive Analysis for Tobacco Cessation: An Exploratory Study

Abstract

Objectives: Predictive analysis can be used to evaluate the enormous data generated by the healthcare industry to extract information and establish relationships amongst the variables. It uses artificial intelligence to reveal associations not suspected by the healthcare professionals. Tobacco cessation is clearly beneficial; however, many tobacco users respond differently as it is based on multitude of factors.  Our objectives were to assess the data mining techniques using the WEKA tool, evaluate its role in predictive analysis, and to predict the quit status of patients using prediction algorithms in tobacco cessation. 

Materials and Methods: WEKA, a data mining tool, was used to classify the data and evaluate them using 10-fold cross-validations. The various algorithms used in this tool are Naïve Bayes, SMO, Random Forest, J-48, and Decision Stump to further analyze its role in determining the quit status of patients. For this, secondary data of 655 patients from a tobacco cessation clinic were utilized and described using 20 different attributes for prediction of quit status.

Results: The Decision Stump and SMO were found to be having the best prediction and accuracy for prediction of the quit status. Out of 20 attributes, previous quitting attempt, type of intervention, and number of years since the habit was initiated were found to be associated with early quitting rate.

Conclusion: This study concluded that data mining and predictive analytical models like WEKA tool will not only improve patient outcomes but identify variables or a combination of variables for effective interventions in tobacco cessation.