Open AccessEmploying Data Mining Techniques for Predicting Opioid Withdrawal in Applicants of Health Centers
Author(s) -
Raheleh Hamedanizad,
Elham Bahmani,
Mojtaba Jamshidi,
Aso Mohammad Darwesh
Publication year - 2019
Publication title -
uhd journal of science and technology
Language(s) - English
Resource type - Journals
eISSN - 2521-4217
pISSN - 2521-4209
DOI - 10.21928/uhdjst.v3n2y2019.pp33-40
Subject(s) - c4.5 algorithm , machine learning , decision tree , computer science , population , artificial intelligence , multilayer perceptron , welfare , addiction , statistical population , artificial neural network , naive bayes classifier , data mining , medicine , psychiatry , statistics , mathematics , environmental health , political science , descriptive statistics , support vector machine , law
Addiction to narcotics is one of the greatest health challenges in today’s world which has become a serious threat for social, economic, and cultural structures and has ruined a part of an active force of the society and it is one of the main factors of growth of diseases such as HIV and hepatitis. Today, addiction is known as a disease and welfare organization, and many of the dependent centers try to help the addicts treat this disease. In this study, using data mining algorithms and based on data collected from opioid withdrawal applicants referring to welfare organization, a prediction model is proposed to predict the success of opioid withdrawal applicants. In this study, the statistical population is comprised opioid withdrawal applicants in a welfare organization. This statistical population includes 26 features of 793 instances including men and women. The proposed model is a combination of meta-learning algorithms (decorate and bagging) and J48 decision tree implemented in Weka data mining software. The efficiency of the proposed model is evaluated in terms of precision, recall, Kappa, and root mean squared error and the results are compared with algorithms such as multilayer perceptron neural network, Naive Bayes, and Random Forest. The results of various experiments showed that the precision of the proposed model is 71.3% which is superior over the other compared algorithms.