Volume 13, Issue 3 (Aug & Sep 2019)
payavard 2019, 13(3): 241-250 |
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Yazdani A, Safaei A A, Safdari R, Zahmatkeshan M. Diagnosis of Breast Cancer Using Decision Tree, Artificial Neural Network and Naive Bayes to Provide a Native Model for Fars Province. payavard 2019; 13 (3) :241-250
URL: http://payavard.tums.ac.ir/article-1-6823-en.html
URL: http://payavard.tums.ac.ir/article-1-6823-en.html
1- Assistant Professor, Department of Health Information Technology, School of Management and Medical Informatics, Shiraz University of Medical Sciences, Shiraz, Iran
2- Assistant Professor, Department of Medical Informatics, School of Medical Sciences, Tarbiat Modarres University, Tehran, Iran ,aa.safaei@modares.ac.ir
3- Professor, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
4- Ph.D. in Health Information Management, Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
2- Assistant Professor, Department of Medical Informatics, School of Medical Sciences, Tarbiat Modarres University, Tehran, Iran ,
3- Professor, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
4- Ph.D. in Health Information Management, Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
Abstract: (3505 Views)
Background and Aim: Breast cancer is the most common type of cancer and the main cause of death from cancer in women worldwide. Technologies such as data mining, have enabled experts in this area to improve decision making in the early diagnosis of the disease. Therefore, the purpose of this research is to develop an automatic diagnostic model for breast cancer by employing data mining methods and selecting the model with the highest accuracy of diagnosis.
Materials and Methods: In this study, 654 available patient records of Motahari breast cancer Clinic in Shiraz" were used as the sample. The number of records was reduced to 621 after the pre-processing operation. These samples had 22 features that ultimately used ten were used as effective features in the design of the model. Three types of Decision tree, Naive Bayes and Artificial neural network were used for diagnosis of breast cancer and 10-fold cross-validation method for constructing and evaluating the model on the collected data set.
Results: The results of the three techniques mentioned all three models showed promising results in detecting breast cancer. Finally, the artificial neural network accounted for the highest accuracy of 94/49%(sensitivity 96/19%, specificity 86/36%) in the diagnosis of breast cancer.
Conclusion: Based on the results of the decision tree, the risk factors such as age, weight, Age of menstruation, menopause, OCP of records duration, and the age of the first pregnancy were among the factors affecting the incidence of breast cancer in women.
Materials and Methods: In this study, 654 available patient records of Motahari breast cancer Clinic in Shiraz" were used as the sample. The number of records was reduced to 621 after the pre-processing operation. These samples had 22 features that ultimately used ten were used as effective features in the design of the model. Three types of Decision tree, Naive Bayes and Artificial neural network were used for diagnosis of breast cancer and 10-fold cross-validation method for constructing and evaluating the model on the collected data set.
Results: The results of the three techniques mentioned all three models showed promising results in detecting breast cancer. Finally, the artificial neural network accounted for the highest accuracy of 94/49%(sensitivity 96/19%, specificity 86/36%) in the diagnosis of breast cancer.
Conclusion: Based on the results of the decision tree, the risk factors such as age, weight, Age of menstruation, menopause, OCP of records duration, and the age of the first pregnancy were among the factors affecting the incidence of breast cancer in women.
Keywords: Breast Cancer, Diagnostic Model, Decision Tree, Naive Bayes, Artificial Neural Network, Risk Factors
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