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Visualization of Clinical Data with Neural Networks, Case Study: Polycystic Ovary Syndrome

Jan-Christian Lehtinen, Jari Forsström, Pertti Koskinen, Tuula-Anneli Penttilä, Timo Järvi, Leena Anttila, Visualization of Clinical Data with Neural Networks, Case Study: Polycystic Ovary Syndrome. TUCS Technical Reports 91, Turku Centre for Computer Science, 1997.

Abstract:

In medicine, the use of neural networks has concentrated mainly on classification problems. Clinicians are often interested in knowing what a patients status is compared to other similar cases. Compared to 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 to 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.

BibTeX entry:

@TECHREPORT{tLeFoKoPe97,
  title = {Visualization of Clinical Data with Neural Networks, Case Study: Polycystic Ovary Syndrome},
  author = {Lehtinen, Jan-Christian and Forsström, Jari and Koskinen, Pertti and Penttilä, Tuula-Anneli and Järvi, Timo and Anttila, Leena},
  number = {91},
  series = {TUCS Technical Reports},
  publisher = {Turku Centre for Computer Science},
  year = {1997},
  ISBN = {951-650-940-1},
}

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