Conventional and Non-Conventional ANNs in Medical Diagnostics

2021 ◽  
pp. 1-39
Author(s):  
Devika G. ◽  
Asha G. Karegowda
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Detailed Analysis ◽  
Computer Technology ◽  
Medical Diagnosis ◽  
Imbalanced Data ◽  
Medical Diagnostics ◽  
Medical Data ◽  
Future Directions ◽  
Data Process

Computer technology advancements in recent days have offered professionals in different fields the ability to gather data, process information, store, and retrieve at a faster rate and make effective decisions. The large collection of data among all various applications including medical diagnosis has paved the need to employ advanced artificial neural networks (ANN). This chapter provides a detailed working view of ANN, covering its various architectures and design techniques in brief. A detailed analysis and summary of medical diagnostics applications using various ANN techniques will be leveraged. Imbalanced data is the major problem with medical data. This chapter briefs on the various methods to handle imbalanced data. Finally, future directions and potential current challenges are suggested for additional applications in neural networks.

Conventional and Non-Conventional ANNs in Medical Diagnostics

2022 ◽  
pp. 881-912
Author(s):  
Devika G. ◽  
Asha G. Karegowda
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Detailed Analysis ◽  
Computer Technology ◽  
Medical Diagnosis ◽  
Imbalanced Data ◽  
Medical Diagnostics ◽  
Medical Data ◽  
Future Directions ◽  
Data Process

Computer technology advancements in recent days have offered professionals in different fields the ability to gather data, process information, store, and retrieve at a faster rate and make effective decisions. The large collection of data among all various applications including medical diagnosis has paved the need to employ advanced artificial neural networks (ANN). This chapter provides a detailed working view of ANN, covering its various architectures and design techniques in brief. A detailed analysis and summary of medical diagnostics applications using various ANN techniques will be leveraged. Imbalanced data is the major problem with medical data. This chapter briefs on the various methods to handle imbalanced data. Finally, future directions and potential current challenges are suggested for additional applications in neural networks.

scholarly journals Artificial neural networks in medical diagnosis

2013 ◽  
Vol 11 (2) ◽  
pp. 47-58 ◽  
Author(s):  
Filippo Amato ◽  
Alberto López ◽  
Eladia María Peña-Méndez ◽  
Petr Vaňhara ◽  
Aleš Hampl ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Medical Diagnosis ◽  
Artificial Neural

Fuzzy sets in modeling of patient’s disease states

2019 ◽  
Vol 0 (9/2019) ◽  
pp. 5-11
Author(s):  
Andrzej Ameljańczyk
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Medical Diagnosis ◽  
Medical Diagnostics ◽  
Medical Data ◽  
Medical Decisions ◽  
Disease States ◽  
Definition Of ◽  
Disease Unit

The paper concerns the mathematical modeling of patient’s disease states and disease unit patterns for the needs of algorithms supporting medical decisions. Due to the specificity of medical data and assessments in the modeling of patient’s disease states as well as diseases, the fuzzy set methodology was used. The paper presents a number of new characteristics of fuzzy sets allowing to assess the quality of medical diagnosis. In addition, a definition of a multi-aspect fuzzy set is presented, which may be useful in supporting medical diagnostics based on multi-criteria similarity models. The presented results can be used in the construction of algorithms for assessing the patient's state of health and mainly in the construction of algorithms for supporting diagnostic processes.

scholarly journals Diagnosing Diabetes Using Artificial Neural Networks

2020 ◽  
Vol 5 (2) ◽  
pp. 221-224
Author(s):  
Joy Oyinye Orukwo ◽  
Ledisi Giok Kabari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Success Rate ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
Diabetes Diagnosis ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Artificial Neural

Diabetes has always been a silent killer and the number of people suffering from it has increased tremendously in the last few decades. More often than not, people continue with their normal lifestyle, unaware that their health is at severe risk and with each passing day diabetes goes undetected. Artificial Neural Networks have become extensively useful in medical diagnosis as it provides a powerful tool to help analyze, model and make sense of complex clinical data. This study developed a diabetes diagnosis system using feed-forward neural network with supervised learning algorithm. The neural network is systematically trained and tested and a success rate of 90% was achieved.

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