VISUALIZATION OF CLINICAL-DATA WITH NEURAL NETWORKS, CASE-STUDY - POLYCYSTIC-OVARY-SYNDROME

In medicine, the use of neural networks has concentrated mainly on cla ssification problems. Clinicians are often interested in knowing what a patient's status is compared with other similar cases. Compared with biostatistics neural networks have one major drawback: the reliabilit y of the classification is difficult to express. Therefore, clear visu alization of the measurements can be more helpful than the calculated probability of a disease. The self-organizing map is the most widely u sed neural network for data visualization. Although, visualization can be attached to almost any feed-forward network as well. In this paper , we describe a topology-preserving feed-forward network and compare i t with the self-organizing map. The two neural network models are used in a case study on the diagnosis of polycystic ovary syndrome, which is a common female endocrine disorder characterized by menstrual abnor malities, hirsutism and infertility. (C) 1997 Elsevier Science Ireland Ltd.