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1

Application of Hardy-Weinberg Law in biomedical research

100%
Jankowska D., Milewski R., Górska U., Milewska A. J.
Studies in Logic, Grammar and Rhetoric
|
2011
|
issue 25(38)
7-28
EN
The aim of this paper is to discuss the Hardy-Weinberg Law which is fundamental for population genetics. It discusses the intuitive expectations connected with the distribution of allele frequencies in a gene pool by using mathematical equations and defines the genetic equilibrium. The conclusions which are consistent with the Hardy-Weinberg Law and relate to biomedical applications seem necessary for the evaluation of data quality. Moreover, ways in which evolutionary forces break genetic equilibrium will be extensively discussed and will be presented using mathematical models of the dynamics of gene pool.
2

Comparison of Artificial Neural Networks and Logistic Regression Analysis in Pregnancy Prediction Using the In Vitro Fertilization Treatment

100%
Milewski R., Milewska A. J., Więsak T., Morgan A.
Studies in Logic, Grammar and Rhetoric
|
2013
|
vol. 35
|
issue 1
39-48
EN
Infertility is recognized as a major problem of modern society. Assisted Reproductive Technology (ART) is the one of many available treatment options to cure infertility. However, the efficiency of the ART treatment is still inadequate. Therefore, the procedure’s quality is constantly improving and there is a need to determine statistical predictors as well as contributing factors to the successful treatment. There is a concern over the application of adequate statistical analysis to clinical data: should classic statistical methods be used or would it be more appropriate to apply advanced data mining technologies? By comparing two statistical models, Multivariable Logistic Regression analysis and Artificial Neural Network it has been demonstrated that Multivariable Logistic Regression analysis is more suitable for theoretical interest but the Artificial Neural Network method is more useful in clinical prediction.
3

Oligonucleotide microarrays in biomedical sciences – the use and data analysis

100%
Milewski R., Milewska A. J., Czerniecki J.
Studies in Logic, Grammar and Rhetoric
|
2011
|
issue 25(38)
29-40
EN
The methods used in biomedical research are becoming inadequate to meet current challenges. Frequently occurring problem is the need to find the differentiation tests according to phenotypic features or the particular phenomenon. Previously used morphological evaluation or other laboratory tests many times do not allow for adequate determination of differentiating attributes. In recent years there has been considerable scientific and technological progress in the fields such as genomics, transcriptomics, proteomics and metabolomics, which allow to move the search area into the molecular level. It allows the use of advanced molecular techniques such as PCR or oligonucleotide microarrays and thus allows to compare the gene expression profiles of different types of cells and tissues. The microarray experiment data allow to determine the correlation between the expression of selected genes or even entire genotypes of the phenotypic features, characterizing the studied group. The collected data can not be analyzed using traditional statistical methods, since the number of cases is much higher than the number of considered attributes. For this reason, new statistical methods and procedures are used for microarray data analysis which may focus on theoretical or practical aspects. Theoretical aspect is related to the selection of specific genes expression, finding the ontology or metabolic pathways that are associated with the analyzed phenomenon. The practical aspect can be the creation of a predictive model that can allow to predict the specific phenonon occurrence in the future during the studies of new patients. Microarray experiments and analysis of the obtained results begin new chapters of particular phenomena investigation, which is another big boost in the biomedical sciences development.
4

The use of the basket analysis in a research of the process of hospitalization in the gynecological ward

88%
Milewska A. J., Górska U., Jankowska D., Milewski R., Wołczyński S.
Studies in Logic, Grammar and Rhetoric
|
2011
|
issue 25(38)
83-98
EN
The progress of science and technology allows to create increasingly complex and detailed databases. It leads to the development of modern data analysis methods. Information collected in medical facilities is characterized by great diversity. In this paper we present a description and application of one of the data mining methods, the basket analysis. It will be used on data describing the process of hospitalization on the gynecological ward. A way of searching for association rules the using basket analysis will be presented. This opens great opportunities for the interpretation obtained results.
5

Clinical department information system’s internal structure

88%
Ziniewicz P., Malinowski P., Milewski R., Mnich S. Z., Wołczyński S.
Studies in Logic, Grammar and Rhetoric
|
2011
|
issue 25(38)
191-200
EN
The construction of an advanced information system supporting the work of a clinic is a great challenge. Particularly, after the initial determination of the functionality of the system, its internal structure must be designed. For the major elements of the system their interaction must be also accurately determined.
6

Significance of Discriminant Analysis in Prediction of Pregnancy in IVF Treatment

76%
Milewska A. J., Jankowska D., Cwalina U., Citko D., Więsak T., Acacio B., Milewski R.
Studies in Logic, Grammar and Rhetoric
|
2015
|
vol. 43
|
issue 1
7-20
EN
Many factors play an important role in prediction of infertility treatment outcome (for example, female age and quality of oocytes or embryos are the most important prognostic factors concerning positive IVF outcome). The purpose of this study was to identify a set of variables that could fulfill criteria for prediction of pregnancy in IVF patients through the application of data mining – using the discriminant analysis method. The principle of this method is to establish a set of rules that allows one to place multi-dimensional objects into one of two analyzed groups (pregnant or not pregnant). Six hundred and ten IVF cycles were included in the analysis and the following variables were taken into consideration: female age, number and quality of retrieved oocytes, number and quality of embryos, number of transferred embryos, and outcome of treatment. Discriminant analysis allowed for the creation of a model with a 51.22% correctness of prediction to achieve pregnancy during IVF treatment and with 74.07% correctly predicted failure of pregnancy. Therefore, the created model is more suitable for the prediction of a negative outcome (lack of pregnancy) during IVF treatment and offers an option for adjustments to be made during infertility treatment.
7

Classification Issue in the IVF ICSI/ET Data Analysis: Early Treatment Outcome Prognosis

76%
Malinowski P., Milewski R., Ziniewicz P., Milewsk A. J., Czerniecki J., Wołczyński S.
Studies in Logic, Grammar and Rhetoric
|
2013
|
vol. 35
|
issue 1
103-115
EN
Infertility is a serious social problem. Very often the only treatment possibility are IVF methods. This study explores the possibility of outcome prediction in the early stages of treatment. The data, collected from the previous treatment cycles, were divided into four subsets, which corresponded to the selected stages of treatment. On each such subset, sophisticated data mining analysis was carried out, with appropriate imputations and classification procedures. The obtained results indicate that there is a possibility of predicting the final outcome at the beginning of treatment.
8

Analyzing Outcomes of Intrauterine Insemination Treatment by Application of Cluster Analysis or Kohonen Neural Networks

76%
Milewska A. J., Jankowska D., Cwalina U., Więsak T., Citko D., Morgan A., Milewski R.
Studies in Logic, Grammar and Rhetoric
|
2013
|
vol. 35
|
issue 1
7-25
EN
Intrauterine insemination (IUI) is one of many treatments provided to infertility patients. Many factors such as, but not limited to, quality of semen, the age of a woman, and reproductive hormone levels contribute to infertility. Therefore, the aim of our study is to establish a statistical probability concerning the prediction of which groups of patients have a very good or poor prognosis for pregnancy after IUI insemination. For that purpose, we compare the results of two analyses: Cluster Analysis and Kohonen Neural Networks. The k-means algorithm from the clustering methods was the best to use for selecting patients with a good prognosis but the Kohonen Neural Networks was better for selecting groups of patients with the lowest chances for pregnancy.
9

Nearest neighbor concept in the study of IVF ICSI/ET treatment effectiveness

76%
Milewski R., Malinowski P., Justyna Milewska A., Czerniecki J., Ziniewicz P., Wołczyński S.
Studies in Logic, Grammar and Rhetoric
|
2011
|
issue 25(38)
49-58
EN
The effectiveness of IVF ICSI/ET infertility treatment depends on many factors. Their identification and classification of individual cases remains a difficult task. This paper presents application of feature selection algorithm MSIMBAF2 and associated kNN classifier to analyze the data set containing results of the infertility treatment process.
10

The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome

76%
Milewska A. J., Jankowska D., Citko D., Więsak T., Acacio B., Milewski R.
Studies in Logic, Grammar and Rhetoric
|
2014
|
vol. 39
|
issue 1
7-23
EN
Principal Component Analysis is one of the data mining methods that can be used to analyze multidimensional datasets. The main objective of this method is a reduction of the number of studied variables with the mainte- nance of as much information as possible, uncovering the structure of the data, its visualization as well as classification of the objects within the space defined by the newly created components. PCA is very often used as a preliminary step in data preparation through the creation of independent components for further analysis. We used the PCA method as a first step in analyzing data from IVF (in vitro fertilization). The next step and main purpose of the analysis was to create models that predict pregnancy. Therefore, 805 different types of IVF cy- cles were analyzed and pregnancy was correctly classified in 61-80% of cases for different analyzed groups in obtained models.
11

The Use of Data Mining Methods to Predict the Result of Infertility Treatment Using the IVF ET Method

76%
Malinowski P., Milewski R., Ziniewicz P., Milewska A. J., Czerniecki J., Wołczyński S.
Studies in Logic, Grammar and Rhetoric
|
2014
|
vol. 39
|
issue 1
67-74
EN
The IVF ET method is a scientifically recognized infertility treat- ment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treat- ment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining gives hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process.
12

Classification of Patients Treated for Infertility Using the IVF Method

64%
Malinowski P., Milewski R., Ziniewicz P., Milewska A. J., Czerniecki J., Więsak T., Morgan A., Surowik D., Wołczyński S.
Studies in Logic, Grammar and Rhetoric
|
2015
|
vol. 43
|
issue 1
49-59
EN
One of the most effective methods of infertility treatment is in vitro fertilization (IVF). Effectiveness of the treatment, as well as classification of the data obtained from it, is still an ongoing issue. Classifiers obtained so far are powerful, but even the best ones do not exhibit equal quality concerning possible treatment outcome predictions. Usually, lack of pregnancy is predicted far too often. This creates a constant need for further exploration of this issue. Careful use of different classification methods can, however, help to achieve that goal.
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