Visualization of clinical data with neural networks, Case study: polycystic ovary syndrome

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• 作者：Lehtinen ; Jan-Christian ; Forsströ ; m ; Jari ; Koskinen ; Pertti ; Penttil&auml ; Tuula-Anneli ; et. al.
• 关键词：Neural networks ; Self-organizing map ; Topology-preserving feed-forward network
• 刊名：International Journal of Medical Informatics
• 出版年：1997
• 出版时间：April, 1997
• 年：1997
• 卷：44
• 期：2
• 页码：145-155
• 全文大小：879 K

文摘

In medicine, the use of neural networks has concentrated mainly on classification 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 reliability of the classification is difficult to express. Therefore, clear visualization of the measurements can be more helpful than the calculated probability of a disease. The self-organizing map is the most widely used 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 it 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 abnormalities, hirsutism and infertility.