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THE STATISTICAL MODULE FOR THE SYSTEM OF ELECTRONIC REGISTRATION OF INFORMATION ABOUT PATIENTS TREATED FOR INFERTILITY USING THE IVF ICSI/ET METHOD

100%
Milewski R., Milewska A. J., Czerniecki J., Jamiolkowski J., Wolczynski S.
Studies in Logic, Grammar and Rhetoric
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2010
|
issue 21(34)
119-127
EN
Infertility treatment using IVF methods requires to the collection, storage and analysis of large quantities of various types of data. Created at the University Hospital in Bialystok, system of electronic registration of information about patients treated for infertility using the IVF ICSI/ET method, turned out to be useful in the process of data collection and storage of information about treated couples. However, it does not satisfy the condition relating to the need to analyze the data collected. For this reason, system developers have taken the trouble of improving it with a statistical module that fulfills hopes connected with it. This module consists of two main parts which generally may be called: descriptive statistics and neural network. The first part of the module refers to the designation and presentation of descriptive statistics. They are based on a number of key features of the treatment process, as well as the juxtaposing the designated statistics, broken down into groups defined by the grouping variables. The second part concerns the neural network to predict the efficacy of the treatment. The network which has been used here provides nearly 90% probability treatment failure and can be used for the prediction of negative cases.
2

A NEURAL NETWORK APPROACH FOR BANK CREDIT DECISIONS (Podejmowanie decyzji kredytowych w oparciu o sztuczne sieci neuronowe)

100%
Rubin B.
Optimum. Studia Ekonomiczne
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2010
|
issue 1(45)
138-152
EN
To grant a bank credit or not is very important for effective bank management. A standard approach of computer decision support systems is based on spread sheets. The artificial neural network approach gives more possibilities to analyze credit decisions, to classify entities applying for credits according to their credibility, including different groups of risk. In this work, the authoress presents a review of various neural network topologies appliance and net trained using various algorithms for dichotomous and polytomic classification. Classification errors were compared and the most effective net was determined. Advantages and disadvantages of described method were shown, which indicate the right appliance of artificial neural networks for the analysis of loan debtors. The usage of artificial networks can rationalize and speed up the process of granting credits, as well as provide a basis for a secondary verification of refused applications.
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