title: Visualization of clinical data with neural networks, case study: polycystic ovary syndrome.
abstract: 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.
authors.affiliation: Department of Computer Science, University of Turku, Finland. jcleh@cs.utu.fi
authors.forename: J C
authors.initials: JC
authors.lastname: Lehtinen
authors.forename: J
authors.initials: J
authors.lastname: Forsström
authors.forename: P
authors.initials: P
authors.lastname: Koskinen
authors.forename: T A
authors.initials: TA
authors.lastname: Penttilä
authors.forename: T
authors.initials: T
authors.lastname: Järvi
authors.forename: L
authors.initials: L
authors.lastname: Anttila
mesh_terms: D000293:Adolescent; D000328:Adult; D001185:Artificial Intelligence; D003626:Data Display; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D005103:Expert Systems; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D010050:Ovarian Function Tests; D011085:Polycystic Ovary Syndrome / Q000145:classification / Q000175:diagnosis*