Showing 4 results for Data Mining
Hamed Mehdizadeh, Alireza Baraani,
Volume 15, Issue 4 (5-2016)
Abstract
Background: Provide a health care service to the patients with diabetes provides useful information that could be used to identify, treatment, following up and prevention of diabetes. Explore and investigation of large volumes of data requires effective and efficient methods for finding hiding patterns in the data. The use of various techniques of data mining in particular Classification and Frequent patterns can be helpful.
Methods: This article is a narrative review. We searched keywords related to application of data mining in the field of diabetes, through related databases, in English language articles published from 2005 to 2015. Also related articles in the selected articles list have been analyzed.
Results: From the 2144 articles obtained in the initial search, 38 articles related to the subject of study, were selected. Several studies shown that classification and clustering algorithms, association rules and artificial intelligence are the most widely used data mining techniques for predict the risk of diabetes has been successfully used.
Conclusion: The important step in control of diabetes, use of the methods that could determine the possibility or lack of diabetes. According to studies conducted in this area seem to use data mining techniques to prevent, treat and discover the connection between diabetes and its risk factors, can lead to significant improvements in the field of diabetes research and provide better health care for this group of patients.
Sadegh Moshrefzadeh, Bahman Ravaei, Ehsan Kozegar,
Volume 21, Issue 2 (7-2021)
Abstract
Background: Diabetes is the fourth leading cause of death in the world. And because so many people around the world have the disease, or are at risk for it, diabetes can be called the disease of the century. Diabetes has devastating effects on the health of people in the community and if diagnosed late, it can cause irreparable damage to vision, kidneys, heart, arteries and so on. Therefore, it is necessary to have methods to diagnose this disease in the early stages. In this article, data mining is used to diagnose diabetes.
Methods: The main algorithm used in this paper is the random forest algorithm. To evaluate the efficiency of the proposed algorithm in diagnosing diabetes, a data set was used that included 768 samples (patients) and had 8 characteristics. Because the stochastic forest algorithm is a hybrid algorithm created from several decision trees, it achieves high accuracy in diagnosing diabetes.
Results: Using this algorithm, we were able to increase the accuracy of diabetes diagnosis to 99.86%.
Conclusion: Diabetes is the fourth leading cause of death in the world. Different algorithms have been used to diagnose this disease. We tried to use an algorithm that has a very high degree of accuracy compared to other algorithms for diagnosing this disease.
Abolfazl Kazemi, Hamid Bahador,
Volume 21, Issue 3 (9-2021)
Abstract
Background: Today, in most hospitals in Iran, there is an extensive database of patient characteristics that includes a large amount of information related to medical, family and medical records. Finding a knowledge model of this information can help to predict the performance of the medical system and improve educational processes.
Methods: Data mining techniques are analytical tools that are used to extract meaningful knowledge from a large data set. In this study, the information of 500 people referred to Shahid Bolandian Health Center in Qazvin has been used. In this research, a predicted model has been performed using decision tree data mining methods and neural network and Bayesian network.
Results: The decision tree model has the highest accuracy and the Bayesian network has the lowest accuracy in diagnosing diabetic patients, and consequently the decision tree has the least error and the Bayesian network has the highest error. The decision tree model with 95.68% had the highest accuracy in prediction.
Conclusion: Fat has the greatest effect in predicting diabetes and gender has the least effect in predicting diabetes. Based on the decision tree analysis, the rules obtained among the stated characteristics of age and sugar variables have the greatest effect in predicting the occurrence of diabetes (according to software analysis) and by creating a proper diet can prevent this disease Prevented.
Fatemeh Dekamini, Mohammad Ehsanifar,
Volume 21, Issue 4 (10-2021)
Abstract
Background: Diabetes is one of the major health problems in Iran and about 4.6 million adults suffer from this disease. Poor diagnosis of this disease has caused half of this number to be unaware of their disease. In recent years, along with the use of computers in data analysis and storage, the volume and complexity of data has increased dramatically.
Methods: In health organizations, data play an essential role in the value of the organization. Therefore, data mining has become one of the most widely used processes in the field of health and disease diagnosis. In this study, the information of 768 laboratory clients in Tehran was kept confidential and the opinions of experts were used to identify the variables affecting the incidence of diabetes.
Results: The findings indicate the study of 5 algorithms on the presented data, which by implementing 5 data mining algorithms J48, Bayes, Beginning, Cohen and simple clustering to classify the data, the efficiency of these algorithms in terms of speed and accuracy in calculations was evaluated.
Conclusion: The data set for classification is the database of a laboratory, which includes 768 samples with 9 characteristics. Finally, J48 algorithm is recommended for data mining of diabetes due to high speed, acceptable accuracy and lack of sensitivity to raw data.
