scholarly journals Employing Data Mining Techniques for Predicting Opioid Withdrawal in Applicants of Health Centers

2019 ◽  
Vol 3 (2) ◽  
pp. 33
Author(s):  
Raheleh Hamedanizad ◽  
Elham Bahmani ◽  
Mojtaba Jamshidi ◽  
Aso Mohammad Darwesh
Keyword(s):  
Data Mining ◽  
Mean Squared Error ◽  
Opioid Withdrawal ◽  
Health Centers ◽  
Statistical Population ◽  
Data Mining Algorithms ◽  
Proposed Model ◽  
Meta Learning ◽  
Using Data ◽  
Mining Algorithms

   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.

scholarly journals A data mining analysis of COVID-19 cases in states of United States of America

2022 ◽  
Vol 12 (2) ◽  
pp. 1754
Author(s):  
Özerk Yavuz
Keyword(s):  
Data Mining ◽  
Mean Squared Error ◽  
Negative Effects ◽  
Classification Rate ◽  
Data Mining Algorithms ◽  
Squared Error ◽  
Using Data ◽  
Algorithmic Technique ◽  
Mining Algorithms ◽  
Epidemic Diseases

Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.

scholarly journals Simple Correlation Between Weather and COVID-19 Pandemic Using Data Mining Algorithms

2020 ◽  
Vol 982 ◽  
pp. 012015
Author(s):  
Ari Fadli ◽  
Azis Wisnu Widhi Nugraha ◽  
Muhammad Syaiful Aliim ◽  
Acep Taryana ◽  
Yogiek Indra Kurniawan ◽  
...  
Keyword(s):  
Data Mining ◽  
Simple Correlation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

scholarly journals Osteoporosis Risk Prediction Using Data Mining Algorithms

2020 ◽  
Author(s):  
Efat Jabarpour ◽  
Amin Abedini ◽  
Abbasali Keshtkar
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Industry Standard ◽  
Disease Information ◽  
Increased Risk ◽  
Using Data ◽  
Mining Algorithms

Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs. Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools. Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for  (TAN) when the precision of  TAN  is higher comparing to other methods. Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.  

Analysis of the Samples with an Unknown Matrix Using Data Mining Algorithms

2017 ◽  
Vol 53 (14) ◽  
pp. 1454-1457
Author(s):  
E. I. Molchanova ◽  
E. N. Korzhova ◽  
T. V. Stepanova ◽  
V. V. Kuz’min
Keyword(s):  
Data Mining ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms
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