Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Mathematica: a system for doing mathematics by computer (2nd ed.)
Mathematica: a system for doing mathematics by computer (2nd ed.)
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Illustrating evolutionary computation with Mathematica
Illustrating evolutionary computation with Mathematica
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Simulating Neural Networks with Mathematica
Simulating Neural Networks with Mathematica
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Paper: Multiple disorder diagnosis with adaptive competitive neural networks
Artificial Intelligence in Medicine
Combining Neural Network and Genetic Algorithm for Prediction of Lung Sounds
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting
Expert Systems with Applications: An International Journal
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Applied Soft Computing
Chest diseases diagnosis using artificial neural networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Diagnosis of chest diseases using artificial immune system
Expert Systems with Applications: An International Journal
| Hi-index | 0.01 |
Background: Genetic algorithms have been used to solve optimization problems for artificial neural networks (ANN) in several domains. We used genetic algorithms to search for optimal hidden-layer architectures, connectivity, and training parameters for ANN for predicting community-acquired pneumonia among patients with respiratory complaints. Methods: Feed-forward back-propagation ANN were trained on sociodemographic, symptom, sign, comorbidity, and radiographic outcome data among 1044 patients from the University of Illinois (the training cohort), and were applied to 116 patients from the University of Nebraska (the testing cohort). Binary chromosomes with genes representing network attributes, including the number of nodes in the hidden layers, learning rate and momentum parameters, and the presence or absence of implicit within-layer connectivity using a competition algorithm, were operated on by various combinations of crossover, mutation, and probabilistic selection based on network mean-square error (MSE), and separately on average cross entropy (ENT). Predictive accuracy was measured as the area under a receiver-operating characteristic (ROC) curve. Results: Over 50 generations, the baseline genetic algorithm evolved an optimized ANN with nine nodes in the first hidden layer, zero nodes in the second hidden layer, learning rate and momentum parameters of 0.5, and no within-layer competition connectivity. This ANN had an ROC area in the training cohort of 0.872 and in the testing cohort of 0.934 (P-value for difference, 0.181). Algorithms based on cross-generational selection, Gray coding of genes prior to mutation, and crossover recombination at different genetic levels, evolved optimized ANN identical to the baseline genetic strategy. Algorithms based on other strategies, including elite selection within generations (training ROC area 0.819), and inversions of genetic material during recombination (training ROC area 0.812), evolved less accurate ANN. Conclusion: ANN optimized by genetic algorithms accurately discriminated pneumonia within a training cohort, and within a testing cohort consisting of cases on which the networks had not been trained. Genetic algorithms can be used to implement efficient search strategies for optimal ANN to predict pneumonia.