Correspondence Analysis of Breast Cancer Diagnosis Classification

There are five indicators used in the diagnosis of breast cancer classification. The indicators are a type of malignancy, location, topography, morphology, behavior, and grade. This study aimed to assess how the relationship between types of diagnostic classification was given to breast cancer patients. The research is a quantitative method and used the hospital medical records were collected in the form of anatomical pathology examination results for hospital patients in the year 2017. Data were obtained from 317 pathology examinations which included 282 breast cancer patients. Each patient is given a diagnosis according to the pathologist's observations into various classifications according to location, topography, type, morphology, grade, and behavior. The result of the analysis showed a relationship between the location of cancer and the type of malignancy. Furthermore, there is a difference in the probability of a malignant or benign tumor-attacking the right or left breast. Correspondence analysis was carried out between the location of the tumor with topography, type of malignancy, morphology, grade, and behavior respectively. The results showed that there was a significant correspondence between topography with the type of malignancy, type of malignancy with morphology, morphology with grade, and grade with behavior. Each type of diagnosis of breast cancer diagnosis is entirely accurate and has a significant correspondence relationship with each other

Analysis of Ordinal Logistic Regression Model on Breast Cancer Diagnosis by Birads Mammography

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

Relative Risk of Coronavirus Disease (Covid-19) in South Sulawesi Province, Indonesia: Bayesian Spatial Modeling

The Covid-19 has exploded in the world since late 2019. South Sulawesi Province has the highest number of Covid-19 cases outside Java Island in Indonesia. This paper aims to determine the most suitable Bayesian spatial conditional autoregressive (CAR) localised models in modeling the relative risk (RR) of Covid-19 in South Sulawesi Province, Indonesia. Bayesian spatial CAR localised models with different hyperpriors were performed adopting a Poisson distribution for the confirmed Covid-19 counts to examine the grouping of Covid-19 cases. All confirmed cases of Covid-19 (19 March 2020-18 February 2021) for each district were included. Overall, Bayesian CAR localised model with G = 5 with a hyperprior IG(1, 0.1) is the preferred model to estimate the RR based on the two criteria used. Makassar and Toraja Utara have the highest and the lowest RR, respectively. The group formed in the localised model is influenced by the magnitude of the mean and variance in the count data between areas. Using suitable Bayesian spatial CAR localised models enables the identification of high-risk areas of Covid-19 cases. This localised model could be applied in other case studies

Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. This shows that there is an improvement of forecasting error rate data.Comment: 13 page