Thesis info Comparative Study of Fuzzy Logic, Artificial Neural Network, and Neuro-Fuzzy System in Medical Diagnostic - An Approach towards a Medical Expert System
Comparative Study of Fuzzy Logic, Artificial Neural Network, and Neuro-Fuzzy System in Medical Diagnostic - An Approach towards a Medical Expert System
Main topic in English
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Title according to student
Comparative Study of Fuzzy Logic, Artificial Neural Network, and Neuro-Fuzzy System in Medical Diagnostic - An Approach towards a Medical Expert System
English title as given by the student
Comparative Study of Fuzzy Logic, Artificial Neural Network, and Neuro-Fuzzy System in Medical Diagnostic - An Approach towards a Medical Expert System
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.
Annotation in English
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.
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.
Annotation in English
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.
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.
Student presented his work in rush and had 32 slides and barely managed the time given.