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Big Data analysis in healthcare
Nováková, Martina ; Kučera, Jan (advisor) ; Chlapek, Dušan (referee)
This thesis deals with the analysis of Big Data in healthcare. The aim is to define the term Big Data, to acquaint the reader with data growth in the world and in the health sector. Another objective is to explain the concept of a data expert and to define team members of the data experts team. In following chapters phases of the Big Data analysis according to methodology of EMC2 company are defined and basic technologies for analysing Big Data are described. As beneficial and interesting I consider the part dealing with definition of tasks in which Big Data technologies are already used in healthcare. In the practical part I perform the Big Data analysis task focusing on meteorotropic diseases in which I use real medical and meteorological data. The reader is not only acquainted with the one of recommended methods of analysis and with used statistical models, but also with terms from the field of biometeorology and healthcare. An integral part of the analysis is also information about its limitations, the consultation on results, and conclusions of experts in meteorology and healthcare.
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Mixed Models and Their Application in Medical Data Analysis
Mohammad, Adam ; Malá, Ivana (advisor) ; Bašta, Milan (referee)
This bachelor thesis deals with the description of the linear mixed model. At first, the model is described theoretically and the basic terms, model specification, estimation of unknown parameters, model-building and model diagnostics are explained. In addition, types of data for which the model may be applied are listed and some examples of applications in medicine are given as well. The second part of this thesis deals with the practical application of the model for analysis of longitudinal data from a medical research. The data contain information about 12 patients to whom a drug named theophylline was given and the concentration of this drug in blood was measured within one day. By statistical software R the dependency of the concentration on time, dose and weight of the patient is described. However the results of the analysis show that the dependency of concentration on time cannot be described by a simple function, therefore this dependency has to be described and the data modified prior to application of linear mixed model on data. The other possibility is to use a different model.
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Geomarketing methods
Voráč, Michal ; Chrobok, Viktor (advisor) ; Černý, Michal (referee)
Aim of this application-oriented master's thesis is to prove a benefits from using data analysis techniques connected with geodata processing to support business decisions. As a conclusion two solutions are given which are more attractive then starting situation. Since the first solution proposed is oriented on giving maximum success ratio while considering transactions, the second one is oriented on business value of each transaction. In this thesis R programming language is widely used together with ArcGIS Online in its final part.
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Gaussian mixtures in R
Marek, Petr ; Malá, Ivana (advisor) ; Zimmermann, Pavel (referee)
Using Gaussian mixtures is a popular and very flexible approach to statistical modelling. The standard approach of maximum likelihood estimation cannot be used for some of these models. The estimates are, however, obtainable by iterative solutions, such as the EM (Expectation-Maximization) algorithm. The aim of this thesis is to present Gaussian mixture models and their implementation in R. The non-trivial case of having to use the EM algorithm is assumed. Existing methods and packages are presented, investigated and compared. Some of them are extended by custom R code. Several exhaustive simulations are run and some of the interesting results are presented. For these simulations, a notion of usual fit is presented.
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Construction of Linear Stochastic Models of SARIMA Class Time Lines – Automatized Method
Trcka, Peter ; Arlt, Josef (advisor) ; Hindls, Richard (referee)
This work concerns the creation of automatized procedure of ARIMA and SARIMA class model choice according to Box-Jenkins methodology and in this connection, also deals with force testing of unit roots and analysis of applying of informatics criteria when choosing a model. The goal of this work is to create an application in the environment R that can automatically choose a model of time array generating process. The procedure is verified by a simulation study. In this work an effect of values of generating ARMA (1,1) model processes parameters is examined, for his choice and power of KPSS test, augmented Dickey-Fuller and Phillips-Peron test of unit roots.
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Utilizing Bootstrap and Cross-validation for prediction error estimation in regression models
Lepša, Ondřej ; Bašta, Milan (advisor) ; Malá, Ivana (referee)
Finding a well-predicting model is one of the main goals of regression analysis. However, to evaluate a model's prediction abilities, it is a normal practice to use criteria which either do not serve this purpose, or criteria of insufficient reliability. As an alternative, there are relatively new methods which use repeated simulations for estimating an appropriate loss function -- prediction error. Cross-validation and bootstrap belong to this category. This thesis describes how to utilize these methods in order to select a regression model that best predicts new values of the response variable.
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Visualization of Multivariate Statistical Data
Maroušek, Vít ; Pecáková, Iva (advisor) ; Černý, Jindřich (referee)
The thesis deals with the possibilities of visualization of multivariate statistical data. Since this is a very broad area the thesis is divided into four sections, two of which are theoretically and two practically oriented. The first section is devoted to theoretical aspects of data visualization. It contains information about the building blocks of graphs, and how the brain processes graphs in various stages of perception. The second section charts the available chart types that can be used to display data. Selected types of graphs for continuous and discontinuous multidimensional data are described in detail. The third section focuses on available software tools for creating graphs. The section describes several programs, with focus on STATISTICA, R and MS Excel. The knowledge gained in previous chapters was sufficient source of information to perform a graphical analysis of multidimensional continuous and discrete data and using advanced analytical methods in the last section. This analysis is performed separately on the data file with continuous variables and on a data file with discontinuous (categorical) variables.
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Data analysis from questionaires about user's satisfaction with NetBeans IDE
Horčic, Michal ; Buchalcevová, Alena (advisor) ; Mirilovič, Marián (referee)
Analysis of the questionaire about user's satisfaction with using IDE NetBeans is main goal of this thesis. More specifically information about dependency (correlation) between questions is going to be result. Process of this analysis is automatical. To reach the goal these technologies are used: - web forms - to collect data from users - MySQL - database system for saving answers from users - R - statistical environment for analysing data The thesis is divided to four parts - Setting of the task - definition of general assumptions and specific setting of the task - Theory - information about statistical method and software that was used - Practice - details about an implementation of the chosen technologies - Evaluation - interpretation of results and their potential exploitation in software planning
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The graphical data analysis in various statistical computing systems
Maroušek, Vít ; Pecáková, Iva (advisor) ; Černý, Jindřich (referee)
Goal of my thesis is to show and compare various ways of graphical data analysis in statistical computing systems. This area is still developing, new techniques are being discovered and the already known are being developed. This work is split into three parts, one theoretical, one comparing and one general with new ideas. The first part is theoretical and is dedicated to classification of statistical variables. For statistical computing systems I offer a simplified classification because of the way they treat some types of variables. Second part of my work compares graphical outputs of three most used statistical computing systems SAS, SPSS and STATISTICA. This comparison is based on some of the most commonly used graphical methods. The outputs are tables with detailed description and examples of graphs from each system are attached. The last part is focused on the general graphical data exploration and follows the simplified classification of variables. In this part I use common graphical techniques and introduce some new or fewer used techniques. Many of them haven't been described in any Czech literature yet. Almost all the graphs presented in this work were created by me in various statistical computing systems. Two were copied from literature because of serious problems with the process of their creation.
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