The Effect of Data Sampling on the Performance Evaluation of Artificial Neural Networks in Medical Diagnosis

1997 ◽  
Vol 17 (2) ◽  
pp. 186-192 ◽  
Author(s):  
Georgia D. Tourassi ◽  
Carey E. Floyd
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Performance Evaluation ◽  
Medical Diagnosis ◽  
Data Sampling ◽  
Artificial Neural

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

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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