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.

scholarly journals ARTIFICIAL NEURAL NETWORKS IN MEDICAL DIAGNOSIS

2021 ◽  
Vol 2 (133) ◽  
pp. 33-41
Author(s):  
Nataliya Matveeva
Keyword(s):  
Neural Networks ◽  
Type 2 Diabetes ◽  
Artificial Neural Networks ◽  
Multilayer Perceptron ◽  
Medical Diagnosis ◽  
Healthy Person ◽  
Modeling Process ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks

Artificial neural networks are finding many uses in the medical diagnosis application. The article examines cases of renopathy in type 2 diabetes. Data are symptoms of disease. The multilayer perceptron networks (MLP) is used as a classifier to distinguish between a sick and a healthy person. The results of applying artificial neural networks for diagnose renopathy based on selected symptoms show the network's ability to recognize to recognize diseases corresponding to human symptoms. Various parameters, structures and learning algorithms of neural networks were tested in the modeling process.

A General Medical Diagnosis System Formed by Artificial Neural Networks and Swarm Intelligence Techniques

2018 ◽  
pp. 130-145 ◽  
Author(s):  
Pandian Vasant
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Swarm Intelligence ◽  
Medical Diagnosis ◽  
Intelligent Systems ◽  
General Framework ◽  
Diagnosis System ◽  
Artificial Neural ◽  
Medical Diagnosis System

One of the most popular applications of artificial intelligence within the medical field is developing medical diagnosis systems. Because artificial-intelligence-based techniques are able to use pre-data and instant data flow for making predictions, it is an easy task to design intelligent systems that can give advice to people or perform diagnosis-based decision making. So, it has been an important research interest to design and develop intelligent systems, which are able to make diagnoses for medical purposes. In this sense, the objective of this chapter is to introduce a general medical diagnosis system that can be used for detecting diseases. In detail, the system employs artificial neural networks and swarm-intelligence-based techniques to form a general framework of intelligent diagnosis. The chapter briefly focuses on the infrastructure of the system and discusses its diagnosis potential.

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