Bayesian Network for Medical Data Analysis
Bayesian Network for Medical Data Analysis
dc.contributor.advisor | Vomlel Jiří | |
dc.contributor.author | Issam Salman | |
dc.date.accessioned | 2024-03-26T12:19:14Z | |
dc.date.available | 2024-03-26T12:19:14Z | |
dc.date.issued | 2024-03-21 | |
dc.identifier | KOS-690664237505 | |
dc.identifier.uri | http://hdl.handle.net/10467/114121 | |
dc.description.abstract | In this paper, we provide an approach to learning optimal Bayesian network (BN) structures from incomplete data based on the BIC score function using a mixture model to handle miss- ing values. We have compared the proposed approach with other methods. Our experiments have been conducted on different models, some of them Belief Noisy-Or (BNO) ones. We have performed experiments using datasets with values missing completely at random having differ- ent missingness rates and data sizes. We have analyzed the significance of differences between the algorithm performance levels using the Wilcoxon test. The new approach typically learns additional edges in the case of Belief Noisy-or models. We have analyzed this issue using the Chi-square test of independence between the variables in the true models; this approach reveals that additional edges can be explained by strong dependence in generated data. An important property of our new method for learning BNs from incomplete data is that it can learn not only optimal general BNs but also specific Belief Noisy-Or models which is using in many applica- tions such as medical application. | cze |
dc.description.abstract | In this paper, we provide an approach to learning optimal Bayesian network (BN) structures from incomplete data based on the BIC score function using a mixture model to handle miss- ing values. We have compared the proposed approach with other methods. Our experiments have been conducted on different models, some of them Belief Noisy-Or (BNO) ones. We have performed experiments using datasets with values missing completely at random having differ- ent missingness rates and data sizes. We have analyzed the significance of differences between the algorithm performance levels using the Wilcoxon test. The new approach typically learns additional edges in the case of Belief Noisy-or models. We have analyzed this issue using the Chi-square test of independence between the variables in the true models; this approach reveals that additional edges can be explained by strong dependence in generated data. An important property of our new method for learning BNs from incomplete data is that it can learn not only optimal general BNs but also specific Belief Noisy-Or models which is using in many applica- tions such as medical application. | eng |
dc.publisher | České vysoké učení technické v Praze. Vypočetní a informační centrum. | cze |
dc.publisher | Czech Technical University in Prague. Computing and Information Centre. | eng |
dc.rights | A university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one?s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://knihovny.cvut.cz/vychova/vskp.html | eng |
dc.rights | Vysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://knihovny.cvut.cz/vychova/vskp.html | cze |
dc.subject | Machine Learning | cze |
dc.subject | Data mining | cze |
dc.subject | Data analysis | cze |
dc.subject | Classification | cze |
dc.subject | Bayesian networks | cze |
dc.subject | Belief Noisy-Or | cze |
dc.subject | Structure learning | cze |
dc.subject | Acute Myocardial Infarction | cze |
dc.subject | Machine Learning | eng |
dc.subject | Data mining | eng |
dc.subject | Data analysis | eng |
dc.subject | Classification | eng |
dc.subject | Bayesian networks | eng |
dc.subject | Belief Noisy-Or | eng |
dc.subject | Structure learning | eng |
dc.subject | Acute Myocardial Infarction | eng |
dc.title | Bayesian Network for Medical Data Analysis | cze |
dc.title | Bayesian Network for Medical Data Analysis | eng |
dc.type | disertační práce | cze |
dc.type | doctoral thesis | eng |
dc.date.accepted | 2024-03-26 | |
dc.contributor.referee | Kozubek Tomáš | |
theses.degree.discipline | Matematické inženýrství | cze |
theses.degree.grantor | katedra softwarového inženýrství | cze |
theses.degree.programme | Aplikace přírodních věd | cze |
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Disertační práce - 14000 [251]