| dc.contributor.advisor | Spittler, Thomas | |
| dc.contributor.author | Kolle, Harvey Ngoe | |
| dc.date.accessioned | 2026-01-06T11:55:31Z | |
| dc.date.available | 2026-01-06T11:55:31Z | |
| dc.date.issued | 2023 | |
| dc.date.submitted | 2023-02-06 | |
| dc.identifier.uri | https://dspace.jcu.cz/handle/20.500.14390/48680 | |
| dc.description.abstract | This study compares the performance of three artificial intelligence techniques (fuzzy logic, artificial neural networks, and neuro-fuzzy systems) in the medical diagnosis of diabetes mellitus, heart disease, and hepatitis B. Medical expert systems were developed using these techniques and evaluated on medical datasets. The results show that neuro-fuzzy systems demonstrate the best performance overall and are the most promising approach for developing accurate and efficient medical expert systems. | cze |
| dc.format | xii p, 94 p. | |
| dc.format | xii p, 94 p. | |
| dc.language.iso | eng | |
| dc.publisher | Jihočeská univerzita | cze |
| dc.rights | Práce není přístupná | |
| dc.subject | fuzzy logic | cze |
| dc.subject | artificial neural networks | cze |
| dc.subject | neuro-fuzzy systems | cze |
| dc.subject | medical diagnosis | cze |
| dc.subject | expert system. | cze |
| dc.subject | fuzzy logic | eng |
| dc.subject | artificial neural networks | eng |
| dc.subject | neuro-fuzzy systems | eng |
| dc.subject | medical diagnosis | eng |
| dc.subject | expert system. | eng |
| dc.title | Comparative Study of Fuzzy Logic, Artificial Neural Network, and Neuro-Fuzzy System in Medical Diagnostic - An Approach towards a Medical Expert System | cze |
| dc.title.alternative | Comparative Study of Fuzzy Logic, Artificial Neural Network, and Neuro-Fuzzy System in Medical Diagnostic - An Approach towards a Medical Expert System | eng |
| dc.type | diplomová práce | cze |
| dc.identifier.stag | 70712 | |
| dc.description.abstract-translated | This study compares the performance of three artificial intelligence techniques (fuzzy logic, artificial neural networks, and neuro-fuzzy systems) in the medical diagnosis of diabetes mellitus, heart disease, and hepatitis B. Medical expert systems were developed using these techniques and evaluated on medical datasets. The results show that neuro-fuzzy systems demonstrate the best performance overall and are the most promising approach for developing accurate and efficient medical expert systems. | eng |
| dc.date.accepted | 2023-03-02 | |
| dc.description.department | Přírodovědecká fakulta | cze |
| dc.thesis.degree-discipline | Artificial Intelligence and Data Science | cze |
| dc.thesis.degree-grantor | Jihočeská univerzita. Přírodovědecká fakulta | cze |
| dc.thesis.degree-name | Mgr. | |
| dc.thesis.degree-program | Artificial Intelligence and Data Science | cze |
| dc.description.grade | Dokončená práce s úspěšnou obhajobou | cze |
| dc.contributor.referee | Berl, Andreas | |
| dc.contributor.referee | Torkler, Phillipp | |
| dc.description.defence | <p>Committee: doc. Dr.rer.nat Jan Valdman, Ing. Rudolf Vohnout, Ph.D., Prof. Dr. Andreas Berl, Prof. Dr. Phillipp Torkler, Mgr. Jakub Geyer, Ing. Ondřej Budík, Dr. Amrit Mukherjee, Ph.D.</p>
<p>Student presented his work in rush and had 32 slides and barely managed the time given.</p>
<p>Could you explain the ranges in your model?</p>
<p> </p> | cze |
