scholarly journals Prediction of kidney disease stages using data mining algorithms

2019 ◽  
Vol 15 ◽  
pp. 100178 ◽  
Author(s):  
El-Houssainy A. Rady ◽  
Ayman S. Anwar
Keyword(s):  
Data Mining ◽  
Kidney Disease ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms ◽  
Disease Stages

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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