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Today, companies and organizations are aware of the fact that customer retention leads to greater profitability. Increasing competition causes the rate of customer churn to grow. Therefore, studying the features influencing the tendency of customer churn is important. In the present study, a hybrid model based on the data mining approach is presented to analyze the features of churn customers. In the first step, the feature selection node has been used to identify the features with higher importance and remove redundant items. Then, the C5.0 Decision Tree and Bayesian network were used to classify the customers into two groups, turning customers, and non-turning customers. These are data mining techniques and terms that can help in forecasting. Finally, the proposed model has been implemented in the chain store industry as a case study. Key findings indicate that both the decision tree model and the Bayesian network can predict churn customers with different accuracies, the area under the receiver operating curve in the decision tree model is greater than the Bayesian network model and it has better performance. The results indicate the optimal efficiency of the proposed method. In addition, the results show that three features of gender, marital status, and age from the set of demographic characteristics and five factors of average monthly income level, number of purchases per month, the share of online shopping, how to get to know the store, type of market-related to customer transaction records are among the most effective factors. [ABSTRACT FROM AUTHOR]

Background and Aim: Today, we are witnessing tremendous advances in medical data mining. The data, by analyzing and discovering the relationships between them, can lead to algorithms that help us prevent or treat many diseases. Meanwhile, genetic diseases have attracted a large part of the attention of the medical world because the birth of children with genetic disorders imposes a great financial, psychological and emotional burden on society. Therefore, the aim of this study was to present an algorithm as a secondary screening test before performing cell and molecular tests. Material and Methods: In this study, we studied 1000 cases of pregnant women who were in moderate or high-risk group after screening tests. Their clinical data were stored, missing data was deleted, and records were integrated. Then, using Clementine software, data mining and data correlation were performed, and finally a suitable algorithm was performed for diagnosing the disease. Genetic mutations were identified as well. Results: By applying five algorithms of neural networks, support vector machine, binary decision tree, multiple decision tree and logistic regression on the data, it was found that the neural network algorithm with 97.522% accuracy had the highest success rate in diagnosis of genetic-chromosomal diseases before birth. Conclusion: The use of genetic algorithm as a screening test causes less people to be candidates for costly and dangerous cellular and molecular tests and can be used as a tool to help detect the disease. [ABSTRACT FROM AUTHOR]

Background and Objective: Paying attention to the health of workers as a significant part of the population is important as they play an important role in the development of the society, which also has caught the attention of government officials and World Health Organization (WHO). Based on the rules and regulations of workers in different occupations, each year they must undergo certain medical tests and examinations to ensure they have sufficient health to perform their duties. This study aimed to predict the results of examinations, extraction of knowledge and identifying patterns and agents that affect workers' health. Materials and Methods:This was a descriptive-analytic study conducted in Tehran among 1267 employees of various occupations who participated in annual examinations of labor medicine in 2017 and 80 variables related to their health and occupational and family background were collected during the examinations. Due to the size and type of data, the C5.0 decision tree method was used to perform data mining and discovery process. Results: Using the C5.0 decision tree, a model with accuracy of 99.05% was introduced. According to this model, variables with the greatest impact on the health of the employees were identified. Hearing status, especially hearing loss at frequencies of 6000 and 4000 Hz, had the greatest impact on the results of employee health examinations. Conclusion: According to the extracted patterns and identification of determinants that had the greatest impact on the result of medical examinations, it is possible to control the specified factors to improve the health of workers. [ABSTRACT FROM AUTHOR]

Background: Data mining is known as a process of discovering and analysing large amounts of data in order to find meaningful rules and trends. In healthcare, data mining offers numerous opportunities to study the unknown patterns in a data set. These patterns can be used to diagnosis, prognosis and treatment of patients by physicians. The main objective of this study was to predict the level of serum ferritin in women with anemia and to specify the basic predictive factors of iron deficiency anemia using data mining techniques. Methods: In this research 690 patients and 22 variables have been studied in women population with anemia. These data include 11 laboratories and 11 clinical variables of patients related to the patients who have referred to the laboratory of Imam Hossein and Shohada-E- Haft Tir hospitals from April 2013 to April 2014. Decision tree technique has been used to build the model. Results: The accuracy of the decision tree with all the variables is 75%. Different combinations of variables were examined in order to determine the best model to predict. Regarding the optimum obtained model of the decision tree, the RBC, MCH, MCHC, gastrointestinal cancer and gastrointestinal ulcer were identified as the most important predictive factors. The results indicate if the values of MCV, MCHC and MCH variables are normal and the value of RBC variable is lower than normal limitation, it is diagnosed that the patient is likely 90% iron deficiency anemia. Conclusion: Regarding the simplicity and the low cost of the complete blood count examination, the model of decision tree was taken into consideration to diagnose iron deficiency anemia in patients. Also the impact of new factors such as gastrointestinal hemorrhoids, gastrointestinal surgeries, different gastrointestinal diseases and gastrointestinal ulcers are considered in this paper while the previous studies have been limited only to assess laboratory variables. The rules of the decision tree model can improve the process of diagnosing and treatment of the patients with iron deficiency anemia and reduce their costs.

Background and Objectives: According to wide mass data collection at medical centers and proper use of it in order to diagnosis of a malady needs to relevant tools and medical science for data analyzing. Infertility diagnosis studied by data mining techniques. Methods: All information had been extract from patientchr('39')s documents of ACECR Center for Infertility Treatment, Qom Branch; 700 sample were selected among 14,242 cases in 15 years of age, duration of infertility, family connections, infertility, family, job, male, female menstrual cycle type, hirsutism, galactorrhea, amenorrhea, cause of infertility, female body mass index, smoking and semen tests were used. The prediction algorithms C5.0, C & R tree, CHAID and K-means clustering algorithm to determine the optimal number of clusters Davis - Buldian used. Results: According to the accepted model, the error is less CHAID algorithm, the most important factor in infertility in the female body mass index, age, disease, hirsutism, infertility, family, illness, galactorrhea, the amount of sperm per milliliter, duration of infertility, old man, were consanguineous couples. According to this model, most of the menchr('39')s wear agents were identified. Conclusion: In this study, the effect of female infertility factors predicted.

Background: Colorectal cancer is one of the most common cancers in terms of morbidity and mortality worldwide. A lot of research have been done in this field in Iran and worldwide, which have positive results. The aims of this study were firstly doing a statistical study on colorectal cancer in Mashhad, Iran, and finally predicting the colorectal location of cancer based on the clinical data by using data mining science and decision tree model. Materials and Methods: The data of 316 patients with colorectal cancer (including 14 features) were extracted from the archive of Imam Reza Hospital, Mashhad. The instrument used in this research was RapidMiner data mining software that would try to be extract the details of the relevant data by statistical surveys and then would do initial simulations and the use of classification and decision tree method have predicteion the location of cancer. Results: Male to female ratio of 56% to 44%, family history of 37%, more young patients, and relatively more distally located cancers (39%) compared with the proximal (35%), and rectum (26%) were the striking findings of this study. The final and most important stage of research models were presented, which was able to predict the location of the cancerous tumor with 80% accuracy. Conclusion: Similarities with global statistics, such as the ratio of men to women and family history were observed. But there were also differences with global statistics including the Iran's younger patients and relatively more patients with distal cancers. The efficiency of data mining techniques to predict the location of cancer as well as cost reduction was among the most important results of this study. [ABSTRACT FROM AUTHOR]

Introduction: Using data mining to develop prediction models from large amounts of data in medicine has become increasingly popular during the recent years. In this study, we intend to use a decision tree data mining algorithm in order to identify factors influencing mortality in burn patients. Methods: The present retrospective descriptive study is based on burning patients’ records. Overall, the medical records of 4804 patients were scrutinized. The collected data were analyzed using statistical software (SPSS version 16), data mining software (Clementine version 12), and CHAID algorithm. Results: The resulting model for predicting survival and mortality of burning patients included the percentage of Total Burn Surface Area (TBSA %), degree of burn, length of stay, gender, and age of patients. Other variables including blood cultures, wound cultures, urine cultures, and the months of patient hospitalization had no effect on improving the efficiency of the model. Conclusion: The proposed model is valid and reliable due to its accuracy (approximately 95%). In fact, the results of this study, some of which are consistent with the results of other studies, can propose a comprehensive, accurate, and reliable local model for predicting mortality and survival of burning patients based on the mentioned variables. Thus, this local model can be used as an important criterion for evaluating the effective treatment of burn patients. [ABSTRACT FROM AUTHOR]

increasing rate of about 6%. More than 2 million people in Iran are now affected by this disease. The present research deals with the relation between the observed complications of type 2 diabetic patients and some related features like Blood Glucose Level, Blood Pressure, Age, and Family History. The main purpose was to predict the patients’ complications based on the observed signs. Methods: The research data were gathered from 856 patient records related to the 2009’s cases in the Diabetes Center of Golestan province. A new model based on the standard methodology CRISP was developed. In the modeling section, two well-known data mining techniques called C5.0 decision tree and Neural Network were used. Celementine 12.0 software was implemented For data analysis. Results: The results of data mining showed that the variables of high blood pressure, age, and family history had the most impact on the observed complications. Based on the created decision tree, some rules have been extracted which can be used as a pattern to predict the probability of occurring these complications in the patients. The accuracy of the C5.0 model on the data was shown to be 89.74% and on the Artificial Neural Network was 51.28%. Conclusion: As the highest accuracy was shown to be achieved using C5.0 algorithm, according to the created rules, it can be predicted which complication(s) any diabetic patient with new specified features may probably suffer from. [ABSTRACT FROM AUTHOR]

The main goal of this study is the prediction of companies listed in Tehran Stock Exchange using decision tree and logistic regression, which are data mining methods and can help facilitate prediction. Financial ratios are independent variables and healthy and bankrupt companies are dependent variables. Statistical population of the research is information about financial statements of companies listed in Tehran Stock Exchange during 1999 - 2010. No sampling was used in this study; we have two groups of healthy and bankrupt companies. Bankrupt companies group was selected based on article 141 of Commercial law and the healthy group was selected based on profitability criteria. Results suggest that both decision tree and logistic regression methods predict bankruptcy with different accuracy. Area under ROC curve in logistic regression model is more than in decision tree model. [ABSTRACT FROM AUTHOR]

Background: Data mining is known as a process of discovering and analysing large amounts of data in order to find meaningful rules and trends. In healthcare, data mining offers numerous opportunities to study the unknown patterns in a data set. These patterns can be used to diagnosis, prognosis and treatment of patients by physicians. The main objective of this study was to predict the level of serum ferritin in women with anemia and to specify the basic predictive factors of iron deficiency anemia using data mining techniques. Methods: In this research 690 patients and 22 variables have been studied in women population with anemia. These data include 11 laboratories and 11 clinical variables of patients related to the patients who have referred to the laboratory of Imam Hossein and Shohada-E- Haft Tir hospitals from April 2013 to April 2014. Decision tree technique has been used to build the model. Results: The accuracy of the decision tree with all the variables is 75%. Different combinations of variables were examined in order to determine the best model to predict. Regarding the optimum obtained model of the decision tree, the RBC, MCH, MCHC, gastrointestinal cancer and gastrointestinal ulcer were identified as the most important predictive factors. The results indicate if the values of MCV, MCHC and MCH variables are normal and the value of RBC variable is lower than normal limitation, it is diagnosed that the patient is likely 90% iron deficiency anemia. Conclusion: Regarding the simplicity and the low cost of the complete blood count examination, the model of decision tree was taken into consideration to diagnose iron deficiency anemia in patients. Also the impact of new factors such as gastrointestinal hemorrhoids, gastrointestinal surgeries, different gastrointestinal diseases and gastrointestinal ulcers are considered in this paper while the previous studies have been limited only to assess laboratory variables. The rules of the decision tree model can improve the process of diagnosing and treatment of the patients with iron deficiency anemia and reduce their costs. [ABSTRACT FROM AUTHOR]