Applicability of Data Mining and Predictive Analysis for Tobacco Cessation: An Exploratory Study
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 cessa...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tehran University of Medical Sciences
2020-11-01
|
| Series: | Frontiers in Dentistry |
| Subjects: | |
| Online Access: | https://fid.tums.ac.ir/index.php/jdt/article/view/2425 |
| _version_ | 1828841427419791360 |
|---|---|
| author | Kavita Rijhwani Vikrant R R Mohanty Aswini YB Vaibhav Singh Sumbul Hashmi |
| author_facet | Kavita Rijhwani Vikrant R R Mohanty Aswini YB Vaibhav Singh Sumbul Hashmi |
| author_sort | Kavita Rijhwani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T20:03:15Z |
| format | Article |
| id | doaj.art-07c51ede671b41d29ea4c3f83ea7fb03 |
| institution | Directory Open Access Journal |
| issn | 2676-296X |
| language | English |
| last_indexed | 2024-12-12T20:03:15Z |
| publishDate | 2020-11-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Frontiers in Dentistry |
| spelling | doaj.art-07c51ede671b41d29ea4c3f83ea7fb032022-12-22T00:13:41ZengTehran University of Medical SciencesFrontiers in Dentistry2676-296X2020-11-012425Applicability of Data Mining and Predictive Analysis for Tobacco Cessation: An Exploratory StudyKavita Rijhwani0Vikrant R R Mohanty1Aswini YB2Vaibhav Singh3Sumbul Hashmi4Department of Public Health Dentistry, Maulana Azad Institute of Dental Sciences, Delhi, IndiaDepartment of Public Health Dentistry, Maulana Azad Institute of Dental Sciences, Delhi, IndiaDepartment of Public Health Dentistry, Maulana Azad Institute of Dental Sciences, Delhi, IndiaDepartment of Computer Science, Rameshwaram Institute of Technology and Management, Lucknow (U.P), IndiaDepartment of Public Health Dentistry, Maulana Azad Institute of Dental Sciences, Delhi, IndiaObjectives: 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.https://fid.tums.ac.ir/index.php/jdt/article/view/2425data miningtobacco use cessationalgorithms |
| spellingShingle | Kavita Rijhwani Vikrant R R Mohanty Aswini YB Vaibhav Singh Sumbul Hashmi Applicability of Data Mining and Predictive Analysis for Tobacco Cessation: An Exploratory Study Frontiers in Dentistry data mining tobacco use cessation algorithms |
| title | Applicability of Data Mining and Predictive Analysis for Tobacco Cessation: An Exploratory Study |
| title_full | Applicability of Data Mining and Predictive Analysis for Tobacco Cessation: An Exploratory Study |
| title_fullStr | Applicability of Data Mining and Predictive Analysis for Tobacco Cessation: An Exploratory Study |
| title_full_unstemmed | Applicability of Data Mining and Predictive Analysis for Tobacco Cessation: An Exploratory Study |
| title_short | Applicability of Data Mining and Predictive Analysis for Tobacco Cessation: An Exploratory Study |
| title_sort | applicability of data mining and predictive analysis for tobacco cessation an exploratory study |
| topic | data mining tobacco use cessation algorithms |
| url | https://fid.tums.ac.ir/index.php/jdt/article/view/2425 |
| work_keys_str_mv | AT kavitarijhwani applicabilityofdataminingandpredictiveanalysisfortobaccocessationanexploratorystudy AT vikrantrrmohanty applicabilityofdataminingandpredictiveanalysisfortobaccocessationanexploratorystudy AT aswiniyb applicabilityofdataminingandpredictiveanalysisfortobaccocessationanexploratorystudy AT vaibhavsingh applicabilityofdataminingandpredictiveanalysisfortobaccocessationanexploratorystudy AT sumbulhashmi applicabilityofdataminingandpredictiveanalysisfortobaccocessationanexploratorystudy |