Background. Obesity is a risk factor for gallbladder disease. The authors retrospectively analyse the prevalence and risk factors of gallbladder disease using logistic regression and artificial neural networks among obese patients in Taiwan.Methods. Artificial neural networks is a popular technique, which can detect complex patterns within data. They have not been applied to risk of gallbladder disease in obese population. We studied the risk factors associated with gallstones in 117 obese patients who were undergoing bariatric surgery between February 1999 and October 2005. Artificial neural networks, constructed with three-layered back-propagation algorithm, were trained to predict the risk of gallbladder disease. Thirty input variables including clinical data (gender, age, body mass index and associated diseases), laboratory evaluation and histopathologic findings of gallbladder were obtained from the patient records. The result was compared with a logistic regression model developed from the same database.Results. Artificial neural networks demonstrated better average classification rate and lower Type II errors than those of logistic regression. The risk factors from both data mining techniques were diastolic blood pressure, inflammatory condition, abnormal glucose metabolism and cholesterolosis. The biological significance of inflammatory condition in obese population requires further investigation.Conclusion. Artificial neural networks might be a useful tool to predict the risk factors and prevalence of gallbladder disease and gallstone development in obese patients on the basis of multiple variables related to laboratory and pathological features. The performance of artificial neural networks was better than traditional modeling techniques. (c) 2007 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Data mining is a very popular technique and has been widely applied in different areas these days. The artificial neural network has become a very popular alternative in prediction and classification tasks due to its associated memory characteristics and generalization capability. However, the relative importance of potential input variables and the long training process have often been criticized and hence limited its application in handling classification problems. The objective of the proposed study is to explore the performance of data classification by integrating artificial neural networks with the multivariate adaptive regression splines (MARS) approach. The rationale under the analyses is firstly to use MARS in modeling the classification problem, then the obtained significant variables are used as the input variables of the designed neural networks model. To demonstrate the inclusion of the obtained important variables from MARS would improve the classification accuracy of the networks, diagnostic tasks are performed on one fine needle aspiration cytology breast cancer data set. As the results reveal, the proposed integrated approach outperforms the results using discriminant analysis, artificial neural networks and multivariate adaptive regression splines and hence provides an efficient alternative in handling breast cancer diagnostic problems.

The objective of the proposed study is to explore the performance of credit scoring using a two-stage hybrid modeling procedure in integrating artificial neural networks and multivariate adaptive regression splines (MARS). The rationale under the analyses is firstly to use MARS in building the credit scoring model, the obtained significant variables are then served as the input nodes of the designed neural networks model. To demonstrate the effectiveness and feasibility of the proposed approach, credit scoring tasks are performed on one bank housing loan dataset. As the results reveal, the proposed hybrid approach outperforms the results using discriminant analysis, logistic regression, artificial neural networks and MARS and hence provides an efficient alternative in handling credit scoring tasks.

Credit scoring has become a very important task as the credit industry has been experiencing tough competition during the past few years. The objective of the proposed study is to explore the performance of credit scoring using two commonly discussed data mining techniques-classification and regression tree (CART) and multivariate adaptive regression splines (MARS). To demonstrate the effectiveness of credit scoring using CART and MARS, credit scoring tasks are performed on one bank credit card data set. As the results reveal, CART and MARS outperform traditional discriminant analysis, logistic regression, and neural networks approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks.

The study investigates the information content of SGX-DT Nikkei 225 futures prices during the non-cash-trading (NCT) period using an artificial neural network model. The cash market closing index, the futures prices from a period in the same trading day and on the following trading day are utilized to determine the appropriate input nodes of a back propagation neural network model in forecasting the opening cash price index. Sensitivity analysis is first employed to address and solve the issue of finding the appropriate network topology. Extensive studies are then performed on the robustness of the constructed network by using different training and testing sample sizes. The effectiveness of the method is demonstrated on data from a 6-month historical record (1998-99). Analytic results demonstrate that the proposed neural network model outperforms a neural network model with the previous day's closing index as the input node and the random walk model forecasts. It, therefore, indicates that there is valuable information involved in futures prices during the NCT period that can be used to forecast the opening cash market price index.

Credit scoring has become a very important task as the credit industry has been experiencing double-digit growth rate during the past few decades. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the decision of network's topology, importance of potential input variables and the long training process has often long been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring by integrating the backpropagation neural networks with traditional discriminant analysis approach. To demonstrate the inclusion of the credit scoring result from discriminant analysis would simplify the network structure and improve the credit scoring accuracy of the designed neural network model, credit scoring tasks are performed on one bank credit card data set. As the results reveal, the proposed hybrid approach converges much faster than the conventional neural networks model. Moreover, the credit scoring accuracies increase in terms of the proposed methodology and outperform traditional discriminant analysis and logistic regression approaches.