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Lehtinen, J.C. et al.

Lehtinen, Forsström, Koskinen, Penttilä, Järvi, Anttila,

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

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.[1]

References

Visualization of clinical data with neural networks, case study: polycystic ovary syndrome. Lehtinen, J.C., Forsström, J., Koskinen, P., Penttilä, T.A., Järvi, T., Anttila, L. International journal of medical informatics. (1997) [Pubmed]

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