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Introduction Breast cancer is one of the most common and worrisome diseases among women all over the world which becomes more difficult with localized relapses and metastases in this disease and the chance of survival is reduced. therefore the effect of various factors on the incidence of metastases can have an important role in the treatment and prevention of disease progression. Materials and Methods In this study, 1959 women with breast cancer who referred to Omid Hospital in Mashhad from 2001 to 2011 were selected and evaluated. To analyze the data, after the imputation of missing data, the method of penalized profile likelihood was used and data analysis was done in the software R. Results The results of the analysis showed that family background, clinical stage, Her2 protein, and T (tumor size) variables had a significant effect on the incidence of metastasis in breast cancer patients. Conclusion According to the results, the stage code and her2 variables, tumor size and family history in all conditions of analysis had a significant effect on the incidence of metastasis in patients with breast.

Most Datasets related to data mining and machine learning contain data with missing values. How to deal with missing values and to provide solutions based on estimating missing values lead to a very important issue in the field of machine learning and data mining. Among data mining algorithm, the C4.5 algorithm has been used repeatedly because of performance being used in various applications and also ability in working and estimating missing values in data sets. Researchers have presented various methods for deal with missing values and estimating it’s amount in a C4.5 data sets which any of their method causes an increase in accuracy of decision tree and there for produce a more effective and efficient decision. In this paper, for estimating missing values in data sets, at the first, we review the previous methods then the proposed approach as a displacement properties method and in the end the accuracy of proposed methods for deletion and average will be comparing.

Background and Objective: Applying the growth curve is the most powerful way for monitoring the growth in children and through this method it would be possible to recognize in time the deviation from the natural growth pattern in children. Falling the data and missing values are general problems in analyzing the growth longitudinal data. Therefore, it is important that by computing the missing values, the data should be completed and directed towards the proper path for analysis. Methods: This 2 year longitudinal study was done on 317 infants (153 boys and 164 girls) in Shiraz during 1996. The information related to growth (Weight, height, round the head, round the arm, and round the chest) at the birth time were collected and 11 visits from the infants’ living houses were done. In order to influence the missing values on the growth charts, 4 methods (Ignoring the missing values, general and individual models of growth curve and multiple imputation) were considered to study. Mean, 3rd, 50th, 97th centiles of raw and smooth weight were computed in boys and the smooth growth charts of their weight were determined and compared based on the four methods. Findings: There was no noticeable difference in the boys’ mean weight at age under study according to growth curve methods and multiple imputation while missing values were ignored. However, the smooth growth charts showed that applying the individual growth curve model (Second level) and multiple imputation causes the noticeable difference between the values of 3rd, 97th centiles and the traditional analysis (Ignoring missing values). Conclusion: Regarding the existence of missing values in growth longitudinal studies, ignoring the missing values for analyzing is not acceptable. Applying the growth curve model method could be considered important in making desirable the analysis and the proper growth path.