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The right diagnosis is needed for appropriate therapy. The diagnosis of breast cancer is quite ambiguous and requires high accuracy. Mammography is a method of diagnosing breast cancer using BIRADS (Breast Imaging-Reporting and Data System) assessment. This study aimed to assess the accuracy of BIRADS classification in the diagnosis of breast cancer and predictors that influence it through a logistic regression model test. The research method was cross sectional study by collecting data from the results of mammography examinations obtained from Medical Record documents, SIRS (Hospital Information Systems), and the radiologist's expertise of mammography. The data came from 47 hospital breast cancer patients that contained information on potential predictors of breast cancer namely tumor location, metastases, age, weight, and education. Logistic regression model analysis was performed to find the best statistical test model for breast cancer diagnosis classification based on BIRADS assessment. The diagnosis classification of BIRADS was consisting of normal, benign, and malignant grades. For this reason, hypothesis testing was conducted with G test for simultaneous model testing. Then, a development of an appropriate logit model by using a partial test. Followed by conducting a suitability and feasibility test model with the Goodness of Fit using the Hosmer-Lemeshow Test. The results of the analysis revealed that the ordinal logistic regression was the best model of BIRADS classification diagnosis with an accuracy value of 52.5%. The result of ordinal logistic regression model for malignant breast cancer:A significant predictor factors were the location of the tumor, age, education, and the work of cancer patients. The conclusion of the diagnosis classification of breast cancer using BIRADS of mammography is quite accurate and assessment of diagnosis classification BIRADS should pay attention to tumor location factors, age, education, and work of breast cancer patients.

The phenomenon encountered occasionally on complications involving spatial data, is that there is a tendency of heteroscedasticity since every region has distinct characteristics. Thus, it requires the approach which is more appropriate with the problem by using the Bayesian method. Bayesian method on spatial autoregressive model to contend the heteroscedasticity by applying prior distribution on variance parameter of error. To detect heteroscedasticity, it is shown from several responses correlating with the predictors. The method abled to estimate some responses is Seemingly Unrelated Regression (SUR). SUR is an econometrics model that used to be being utilized in solving some regression equations in which of them has their own parameter and appears to be uncorrelated. However, by correlation of error in differential equations, the correlation would occur among them. With the condition of the Bayesian SUR spatial autoregressive model, it is able to overcome heteroscedasticity cases from the vision of spatial. Further, the model involves four kinds of parameter priors’ distributions estimated by using the process of MCMC.

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