M. Saffarian; V. Babaiyan; K. Namakin; F. Taheri; T. Kazemi
Abstract
Today, Metabolic Syndrome in the age group of children and adolescents has become a global concern. In this paper, a data mining model is used to determine a continuous Metabolic Syndrome (cMetS) score using Linear Discriminate Analysis (cMetS-LDA). The decision tree model is used to specify the calculated ...
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Today, Metabolic Syndrome in the age group of children and adolescents has become a global concern. In this paper, a data mining model is used to determine a continuous Metabolic Syndrome (cMetS) score using Linear Discriminate Analysis (cMetS-LDA). The decision tree model is used to specify the calculated optimal cut-off point cMetS-LDA. In order to evaluate the method, multilayer perceptron neural network (NN) and Support Vector Machine (SVM) models were used and statistical significance of the results was tested with Wilcoxon signed-rank test. According to the results of this test, the proposed CART is significantly better than the NN and SVM models. The ranking results in this study showed that the most important risk factors in making cMetS-LDA were WC, SBP, HDL and TG for males and WC, TG, HDL and SBP for females. Our research results show that high TG and central obesity have the greatest impact on MetS and FBS has no effect on the final prognosis. The results also indicate that in the preliminary stages of MetS, WC, HDL and SBP are the most important influencing factors that play an important role in forecasting.
B.3. Communication/Networking and Information Technology
V. Babaiyan; Seyyede A. Sarfarazi
Abstract
Telecommunication Companies use data mining techniques to maintain good relationships with their existing customers and attract new customers and identifying profitable/unprofitable customers. Clustering leads to better understanding of customer and its results can be used to definition and decision-making ...
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Telecommunication Companies use data mining techniques to maintain good relationships with their existing customers and attract new customers and identifying profitable/unprofitable customers. Clustering leads to better understanding of customer and its results can be used to definition and decision-making for promotional schemes. In this study, we used the 999-customer purchase records in South Khorasan Telecommunication Company which has been collected during a year. The purpose of this study is to classify customers into several clusters. Since the clusters and the number of their members are determined, high-consumption users will be logged out of the system and high-value customers who are missed will be identified. In this research we divided our customers into five categories: loyal, potential, new, missed and high-consumption by using the Clementine software, developing the RFM model to the LRFM model and TwoStep and k_Means algorithms. Thus, this category will be a good benchmark for company's future decisions and we can make better decisions for each group of customers in the future.
