Rijhwani Kavita, Mohanty Vikrant R, Yb Aswini, Singh Vaibhav, Hashmi Sumbul
Department of Public Health Dentistry, Maulana Azad Institute of Dental Sciences, Delhi, India.
Department of Computer Science, Rameshwaram Institute of Technology and Management, Lucknow (U.P), India.
Front Dent. 2020 Oct 20;17:24. doi: 10.18502/fid.v17i24.4624. eCollection 2020.
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.
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.
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.
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.
预测分析可用于评估医疗行业产生的海量数据，以提取信息并建立变量之间的关系。它利用人工智能揭示医疗专业人员未曾怀疑的关联。戒烟显然有益；然而，许多吸烟者的反应各不相同，因为这取决于多种因素。我们的目标是评估使用WEKA工具的数据挖掘技术，评估其在预测分析中的作用，并使用戒烟预测算法预测患者的戒烟状态。
使用数据挖掘工具WEKA对数据进行分类，并使用10折交叉验证对其进行评估。该工具中使用的各种算法包括朴素贝叶斯、SMO、随机森林、J-48和决策树桩，以进一步分析其在确定患者戒烟状态中的作用。为此，利用了一家戒烟诊所655名患者的二次数据，并使用20种不同属性对其进行描述，以预测戒烟状态。
发现决策树桩和SMO在预测戒烟状态方面具有最佳的预测能力和准确性。在20个属性中，既往戒烟尝试、干预类型以及开始吸烟以来的年数与早期戒烟率相关。
本研究得出结论，像WEKA工具这样的数据挖掘和预测分析模型不仅会改善患者预后，还能识别变量或变量组合，以有效干预戒烟。
