An investigation of neural networks in thyroid function diagnosis.

We investigate the potential of artificial neural networks in diagnosing thyroid diseases. The robustness of neural networks with regard to sampling variations is examined using a cross-validation method. We illustrate the link between neural networks and traditional Bayesian classifiers. Neural networks can provide good estimates of posterior probabilities and hence can have better classification performance than traditional statistical methods such as logistic regression. The neural network models are further shown to be robust to sampling variations. It is demonstrated that for medical diagnosis problems where the data are often highly unbalanced, neural networks can be a promising classification method for practical use.