Rüdiger W. Brause

Neural data mining for credit card fraud detection

The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: since only one financial transaction in a thousand is invalid no prediction success less than 99.9% is acceptable. Because of these credit card transaction requirements, completely new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and a neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.

Medical Analysis and Diagnosis by Neural Networks

In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic systems. Then, paradigm of neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. Additionally, the problem of interfacing the network and its result is given and the neurofuzzy approach is presented. Finally, as case study of neural rule based diagnosis septic shock diagnosis is described, on one hand by a growing neural network and on the other hand by a rule based system.

Handwriting analysis for diagnosis and prognosis of parkinson's disease

At present, there are no quantitative, objective methods for diagnosing the Parkinson disease. Existing methods of quantitative analysis by myograms suffer by inaccuracy and patient strain; electronic tablet analysis is limited to the visible drawing, not including the writing forces and hand movements. In our paper we show how handwriting analysis can be obtained by a new electronic pen and new features of the recorded signals. This gives good results for diagnostics.

Septic Shock Diagnosis by Neural Networks and Rule Based Systems

In intensive care units physicians are aware of a high lethality rate of septic shock patients. In this contribution we present typical problems and results of a retrospective, data driven analysis based on two neural network methods applied on the data of two clinical studies.

Data quality aspects of a database for abdominal septic shock patients

Since many years, medical researchers have investigated the mechanisms that may cause a septic shock. Despite many approaches that analyzed smaller parts of the relevant data or single variables, respectively, no larger database with all the possible relevant data existed. Our work was to bridge this gap. We built a large database for abdominal septic shock patients. While building it, we were confronted with many problems concerning the database realization and the data quality. Thus, we will demonstrate how we built our database and how we assured data quality. This is of interest for all medical or computer scientists who are concerned with building medical databases with retrospective data, e.g. for data mining purposes.

Review of A Large Clinical Series: Predicting Death for Patients With Abdominal Septic Shock

This paper reports the result of the MEDAN project that analyzes a multicenter septic shock patient data collection. The mortality prognosis based on 4 scores that are often used is compared with the prognosis of a trained neural network. We built an alarm system using the network classification results. Method. We analyzed the data of 382 patients with abdominal septic shock who were admitted to the intensive care unit (ICU) from 1998 to 2002. The analysis includes the calculation of daily sepsis-related organ failure assessment (SOFA), Acute Physiological and Chronic Health Evaluation (APACHE) II, simplified acute physiology score (SAPS) II, multiple-organ dysfunction score (MODS) scores for each patient and the training and testing of an appropriate neural network. Results. For our patients with abdominal septic shock, the analysis shows that it is not possible to predict their individual fate correctly on the day of admission to the ICU on the basis of any current score. However, when the trained network computes a score value below the threshold during the ICU stay, there is a high probability that the patient will die within 3 days. The trained neural network obtains the same outcome prediction performance as the best score, the SOFA score, using narrower confidence intervals and considering three variables only: systolic blood pressure, diastolic blood pressure and the number of thrombocytes. We conclude that the currently best available score for abdominal septic shock may be replaced by the output of a trained neural network with only 3 input variables.

Cascaded vector quantization by non-linear PCA network layers

The different mechanisms of principal component analysis (PCA) and vector quantization are combined in an architecture of one functional layer which implements vector quantization without using winner-take-all nets. After introducing cascaded vector quantization, the paper introduces a new network (the binary cascade network) which is composed of lateral inhibited neurons for PCA. They have bell-shaped activation functions which provide binary cascaded quantization stages. It is shown that this architecture is nearly optimal in terms of resource distribution.<<ETX>>

Adaptive modeling of biochemical pathways

In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data.

A Frequent Patterns Tree Approach for Rule Generation with Categorical Septic Shock Patient Data

In abdominal intensive care medicine letality of septic shock patients is very high. In this contribution we present results of a data driven rule generation with categorical septic shock patient data, collected from 1996 to 1999. Our descriptive approach includes preprocessing of data for rule generation and application of an efficient algorithm for frequent patterns generation. Performance of generated rules is rated by frequency and confidence measures. The best rules are presented. They provide new quantitative insight for physicians with regard to septic shock patient outcome.

Using Growing RBF-Nets in Rubber Industry Process Control

This paper describes the use of a Radial Basis Function (RBF) neural network in the approximation of process parameters for the extrusion of a rubber profile in tyre production. After introducing the rubber industry problem, the RBF network model and the RBF net learning algorithm are developed, which uses a growing number of RBF units to compensate the approximation error up to the desired error limit. Its performance is shown for simple analytic examples. Then the paper describes the modelling of the industrial problem. Simulations show good results, even when using only a few training samples. The paper is concluded by a discussion of possible systematic error influences, improvements and potential generalisation benefits.