Comparison of the Efficiency of Data Mining Algorithms in Predicting the Diagnosis of Diabetes

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.