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.
Lehtinen
J C
JC
Department of Computer Science, University of Turku, Finland. jcleh@cs.utu.fi
Forsström
J
J
Koskinen
P
P
Penttilä
T A
TA
Järvi
T
T
Anttila
L
L
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*